diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json
index 83b92a0..8e1628b 100644
--- a/dev/.documenter-siteinfo.json
+++ b/dev/.documenter-siteinfo.json
@@ -1 +1 @@
-{"documenter":{"julia_version":"1.9.3","generation_timestamp":"2023-10-16T12:03:36","documenter_version":"1.1.1"}}
\ No newline at end of file
+{"documenter":{"julia_version":"1.10.4","generation_timestamp":"2024-08-19T14:06:53","documenter_version":"1.5.0"}}
\ No newline at end of file
diff --git a/dev/LEQ.ipynb b/dev/LEQ.ipynb
index 2067376..0aa2208 100644
--- a/dev/LEQ.ipynb
+++ b/dev/LEQ.ipynb
@@ -25,6 +25,35 @@
"- How to fix static load imbalance"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "480af594",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Note: Do not forget to execute the cell below before starting this notebook! \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e93809a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "using Printf\n",
+ "function answer_checker(answer,solution)\n",
+ " if answer == solution\n",
+ " \"🥳 Well done! \"\n",
+ " else\n",
+ " \"It's not correct. Keep trying! 💪\"\n",
+ " end |> println\n",
+ "end\n",
+ "ge_par_check(answer) = answer_checker(answer, \"a\")\n",
+ "ge_dep_check(answer) = answer_checker(answer, \"b\")"
+ ]
+ },
{
"cell_type": "markdown",
"id": "8dcee319",
@@ -33,7 +62,7 @@
"## Gaussian elimination\n",
"\n",
"\n",
- "System of linear algebraic equations\n",
+ "[Gaussian elimination](https://en.wikipedia.org/wiki/Gaussian_elimination) is a method to solve systems of linear equations, e.g.\n",
"\n",
"$$\n",
"\\left[\n",
@@ -60,7 +89,7 @@
"\\right]\n",
"$$\n",
"\n",
- "Elimination steps\n",
+ "The steps of the Gaussian elimination will transform the system into an upper triangular matrix. The system of linear equations can now easily be solved by backward substitution. \n",
"\n",
"\n",
"$$\n",
@@ -112,7 +141,10 @@
"id": "94c10106",
"metadata": {},
"source": [
- "### Serial implementation\n"
+ "### Serial implementation\n",
+ "The following algorithm computes the Gaussian elimination on a matrix which represents a system of linear equations.\n",
+ "- The first inner loop in line 4 divides the current row by the value of the diagonal entry, thus transforming the diagonal to contain only ones. \n",
+ "- The second inner loop beginning in line 8 substracts the rows from one another such that all entries below the diagonal become zero. "
]
},
{
@@ -140,6 +172,24 @@
"end"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "3763b000",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Note: This algorithm is not correct for all matrices: if any diagonal element B[k,k] is zero, the computation in the first inner loop fails. To get around this problem, another step can be added to the algorithm that swaps the rows until the diagonal entry of the current row is not zero. This process of finding a nonzero value is called pivoting. \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fbb3d1eb",
+ "metadata": {},
+ "source": [
+ "You can verify that the algorithm computes the upper triangular matrix correctly for the example in the introduction by running the following code cell. "
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -185,6 +235,32 @@
"```"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "e52c4b38",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Question: Which of the loops can be parallelized?\n",
+ "
\n",
+ "\n",
+ " a) the inner loops, but not the outer loop\n",
+ " b) the outer loop, but not the inner loops\n",
+ " c) all loops\n",
+ " d) only the first inner loop"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "078e974e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "answer = \"x\" # replace x with a, b, c, or d \n",
+ "ge_par_check(answer)"
+ ]
+ },
{
"cell_type": "markdown",
"id": "14d57c52",
@@ -193,70 +269,79 @@
"### Two possible data partitions"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "6b17aee4",
+ "metadata": {},
+ "source": [
+ "The outer loop of the algorithm is not parallelizable, since the iterations depend on the results of the previous iterations. However, we can extract parallelism from the inner loops. Let's have a look at two different parallelization schemes. \n",
+ "\n",
+ "1. **Block-wise partitioning**: Each processor gets a block of subsequent rows. \n",
+ "2. **Cyclic partitioning**: The rows are alternately assigned to different processors. "
+ ]
+ },
{
"attachments": {
- "g23933.png": {
- "image/png": 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"
+ "fig-asp-1d-partition.png": {
+ "image/png": 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"
}
},
"cell_type": "markdown",
- "id": "f06f0869",
+ "id": "c518f863",
"metadata": {},
"source": [
"
\n",
- "\n",
+ "\n",
"
"
]
},
{
+ "cell_type": "markdown",
+ "id": "102d6fa2",
+ "metadata": {},
+ "source": [
+ "## What is the work per process at iteration k?\n",
+ "To evaluate the efficiency of both partitioning schemes, consider how much work the processors do in the following example. \n",
+ "In any iteration k, which part of the matrix is updated in the inner loops? \n",
+ "\n",
+ "### Block-wise partition"
+ ]
+ },
+ {
+ "attachments": {
+ "fig-asp-data-updated.png": {
+ "image/png": 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"
+ }
+ },
"cell_type": "markdown",
"id": "a67e0aad",
"metadata": {},
- "source": [
- "### Which is the work per process at iteration k ?\n",
- "\n"
- ]
- },
- {
- "attachments": {
- "g26595.png": {
- "image/png": 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"
- }
- },
- "cell_type": "markdown",
- "id": "53a94ed3",
- "metadata": {},
"source": [
"
\n",
- "\n",
+ "\n",
"
"
]
},
{
+ "cell_type": "markdown",
+ "id": "d9d29899",
+ "metadata": {},
+ "source": [
+ "It is clear from the code that at any given iteration `k`, the matrix is updated from row `k` to `n` and from column `k` to `m`. If we look at how that reflects the distribution of work over the processes, we can see that CPU 1 does not have any work, whereas CPU 2 does a little work and CPU 3 and 4 do a lot of work. "
+ ]
+ },
+ {
+ "attachments": {
+ "fig-asp-data-updated-2.png": {
+ "image/png": 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"
+ }
+ },
"cell_type": "markdown",
"id": "d083cd53",
"metadata": {},
"source": [
"
"
]
},
@@ -267,51 +352,74 @@
"source": [
"### Load imbalance\n",
"\n",
- "- CPUs with rows \n",
+ "Question: What are the data dependencies in the block-wise partitioning?\n",
+ "\n",
+ "\n",
+ " a) CPUs with rows >k need all rows <=k\n",
+ " b) CPUs with rows >k need part of row k\n",
+ " c) All CPUs need row k \n",
+ " d) CPUs with row k needs all rows >k \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0565e92",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "answer = \"x\" # replace x with a, b, c, or d \n",
+ "ge_dep_check(answer)"
]
},
{
"cell_type": "markdown",
- "id": "b90252f1",
+ "id": "51498a44",
"metadata": {},
"source": [
"### Cyclic partition\n",
"\n",
- "- Less load imbalance\n",
- "- Same data dependencies as 1d block partition\n",
- "- Useful for some problems with predictable load imbalance\n",
- "- A form of static load balancing\n",
- "- Not suitable for all communication patterns (e.g. Jacobi)"
+ "In contrast, if we look at how the work is balanced for the same example and cyclic partitioning, we find that the processes have similar work load. "
]
},
{
"attachments": {
- "g27009.png": {
- "image/png": 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"
+ "fig-asp-data-updated-cyclic.png": {
+ "image/png": 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"
}
},
"cell_type": "markdown",
- "id": "6a4c4051",
+ "id": "b90252f1",
"metadata": {},
"source": [
"
\n",
- "\n",
+ "\n",
"
"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "866824c6",
+ "metadata": {},
+ "source": [
+ "## Conclusion\n",
+ "Cyclic partitioning tends to work well in problems with predictable load imbalance. It is a form of **static load balancing** which means using a pre-defined load schedule based on prior information about the algorithm (as opposed to **dynamic load balancing** which can schedule loads flexibly during runtime). The data dependencies are the same as for the 1d block partitioning.\n",
+ "\n",
+ "At the same time, cyclic partitioning is not suitable for all communication patterns. For example, it can lead to a large communication overhead in the parallel Jacobi method, since the computation of each value depends on its neighbouring elements."
+ ]
+ },
{
"cell_type": "markdown",
"id": "20982b04",
"metadata": {},
"source": [
"## Exercise\n",
- "\n",
- "- The actual parallel implementation is let as an exercise\n",
- "- Implement both 1d block and 1d cyclic partitions and compare performance\n",
- "- Closely related with Floyd's algorithm\n",
- "- Generate input matrix with function below (a random matrix is not enough, we need a non singular matrix that does not require pivoting)"
+ "The actual implementation of the parallel algorithm is left as an exercise. Implement both 1d block and 1d cyclic partitioning and compare their performance. The implementation is closely related to that of Floyd's algorithm. To test your algorithms, generate input matrices with the function below (a random matrix is not enough, we need a non singular matrix that does not require pivoting). "
]
},
{
@@ -343,16 +451,37 @@
"metadata": {},
"outputs": [],
"source": [
- "n = 5\n",
+ "n = 12\n",
"C = tridiagonal_matrix(n)\n",
"b = ones(n)\n",
- "gaussian_elimination!(C)"
+ "B = [C b]\n",
+ "gaussian_elimination!(B)"
]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f60d9ea0",
+ "metadata": {},
+ "source": [
+ "# License\n",
+ "\n",
+ "\n",
+ "\n",
+ "This notebook is part of the course [Programming Large Scale Parallel Systems](https://www.francescverdugo.com/XM_40017) at Vrije Universiteit Amsterdam and may be used under a [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab22f67",
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
"kernelspec": {
- "display_name": "Julia 1.9.0",
+ "display_name": "Julia 1.9.1",
"language": "julia",
"name": "julia-1.9"
},
@@ -360,7 +489,7 @@
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
- "version": "1.9.0"
+ "version": "1.9.1"
}
},
"nbformat": 4,
diff --git a/dev/LEQ/index.html b/dev/LEQ/index.html
index fa13ca3..5ed7f23 100644
--- a/dev/LEQ/index.html
+++ b/dev/LEQ/index.html
@@ -1,5 +1,5 @@
-Gaussian elimination · XM_40017
The steps of the Gaussian elimination will transform the system into an upper triangular matrix. The system of linear equations can now easily be solved by backward substitution.
The following algorithm computes the Gaussian elimination on a matrix which represents a system of linear equations.
+
+
The first inner loop in line 4 divides the current row by the value of the diagonal entry, thus transforming the diagonal to contain only ones.
+
The second inner loop beginning in line 8 substracts the rows from one another such that all entries below the diagonal become zero.
+
@@ -7621,6 +7695,30 @@ $$
+
+
+
+
+
+
+
+
+Note: This algorithm is not correct for all matrices: if any diagonal element B[k,k] is zero, the computation in the first inner loop fails. To get around this problem, another step can be added to the algorithm that swaps the rows until the diagonal entry of the current row is not zero. This process of finding a nonzero value is called pivoting.
+
+
+
+
+
+
+
+
+
+
+
+
You can verify that the algorithm computes the upper triangular matrix correctly for the example in the introduction by running the following code cell.
+
+
+
@@ -7674,6 +7772,38 @@ $$
+
+
+
+
+
+
+
+Question: Which of the loops can be parallelized?
+
+
a) the inner loops, but not the outer loop
+b) the outer loop, but not the inner loops
+c) all loops
+d) only the first inner loop
+
+
+
+
+
+
+
+
+
In [ ]:
+
+
+
answer="x"# replace x with a, b, c, or d
+ge_par_check(answer)
+
+
+
+
+
+
@@ -7685,39 +7815,67 @@ $$
-
+
+
+
+
+
+
+
The outer loop of the algorithm is not parallelizable, since the iterations depend on the results of the previous iterations. However, we can extract parallelism from the inner loops. Let's have a look at two different parallelization schemes.
+
+
Block-wise partitioning: Each processor gets a block of subsequent rows.
+
Cyclic partitioning: The rows are alternately assigned to different processors.
To evaluate the efficiency of both partitioning schemes, consider how much work the processors do in the following example.
+In any iteration k, which part of the matrix is updated in the inner loops?
It is clear from the code that at any given iteration k, the matrix is updated from row k to n and from column k to m. If we look at how that reflects the distribution of work over the processes, we can see that CPU 1 does not have any work, whereas CPU 2 does a little work and CPU 3 and 4 do a lot of work.
The block-wise partitioning scheme leads to load imbalance across the processes: CPUs with rows $<k$ are idle during any iteration $k$. The bad load balance leads to bad speedups, as some CPUs are waiting instead of doing useful work.
Cyclic partitioning tends to work well in problems with predictable load imbalance. It is a form of static load balancing which means using a pre-defined load schedule based on prior information about the algorithm (as opposed to dynamic load balancing which can schedule loads flexibly during runtime). The data dependencies are the same as for the 1d block partitioning.
+
At the same time, cyclic partitioning is not suitable for all communication patterns. For example, it can lead to a large communication overhead in the parallel Jacobi method, since the computation of each value depends on its neighbouring elements.
The actual implementation of the parallel algorithm is left as an exercise. Implement both 1d block and 1d cyclic partitioning and compare their performance. The implementation is closely related to that of Floyd's algorithm. To test your algorithms, generate input matrices with the function below (a random matrix is not enough, we need a non singular matrix that does not require pivoting).
\n",
+ "Note: Do not forget to run the next cell before starting studying this notebook. \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1dc78750",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "using Printf\n",
+ "\n",
+ "function answer_checker(answer,solution)\n",
+ " if answer == solution\n",
+ " \"🥳 Well done! \"\n",
+ " else\n",
+ " \"It's not correct. Keep trying! 💪\"\n",
+ " end |> println\n",
+ "end\n",
+ "floyd_check(answer) = answer_checker(answer,\"c\")\n",
+ "floyd_impl_check(answer) = answer_checker(answer, \"d\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "24b5c21a",
"metadata": {},
"source": [
"## The All Pairs of Shortest Paths (ASP) problem\n",
"\n",
- "Let us start by presenting the all pairs of shortest paths (ASP) problem and its solution with the [Floyd–Warshall algorithm](https://en.wikipedia.org/wiki/Floyd%E2%80%93Warshall_algorithm).\n",
+ "Let us start by presenting the all pairs of shortest paths (ASP) problem and its solution, the [Floyd–Warshall algorithm](https://en.wikipedia.org/wiki/Floyd%E2%80%93Warshall_algorithm).\n",
"\n",
"### Problem statement\n",
"\n",
"- Given a graph $G$ with a distance table $C$\n",
- "- Compute the length of the shortest path between any two nodes in $G$\n",
- "\n",
- "We represent the distance table as a matrix, where $C_{ij}$ is the distance from node $i$ to node $j$. Next figure shows the input and solution (output) of the ASP problem for a simple 4-node directed graph. Note that the minimum distance from node 2 to node 3, which is $C_{23}=8$ as highlighted in the figure.\n"
+ "- Compute the length of the shortest path between any two nodes in \n",
+ "$G$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ade31d26",
+ "metadata": {},
+ "source": [
+ "We represent the distance table as a matrix, where $C_{ij}$ is the distance from node $i$ to node $j$. The next figure shows the input and solution (output) of the ASP problem for a simple 4-node directed graph. Note that the minimum distance from node 2 to node 3, $C_{23}=8$, is highlighted in the figure."
]
},
{
@@ -229,7 +266,7 @@
"source": [
"### Serial performance\n",
"\n",
- "This algorithm is memory bound, meaning that the main cost is in getting and setting data from the input matrix `C`. In this situations, the order in which we traverse the entries of matrix `C` has a significant performance impact.\n",
+ "This algorithm is memory bound, meaning that the main cost is in getting and setting data from the input matrix `C`. In this situation, the order in which we traverse the entries of matrix `C` has a significant performance impact.\n",
"\n",
"The following function computes the same result as for the previous function `floyd!`, but the nesting of loops over i and j is changed.\n"
]
@@ -282,7 +319,7 @@
"id": "ad811b10",
"metadata": {},
"source": [
- "The performance difference is significant. Matrices in Julia are stored in memory in column-major order (like in Fortran, unlike in C). It means that it is more efficient to access the data also in column-major order (like in function `floyd!`). See this section of [Julia's performance tips](https://docs.julialang.org/en/v1/manual/performance-tips/#man-performance-column-major) if you are interested in further details."
+ "The performance difference is significant. Matrices in Julia are stored in memory in column-major order (like in Fortran, unlike in C and Python). It means that it is more efficient to access the data also in column-major order (like in function `floyd!`). See this section of [Julia's performance tips](https://docs.julialang.org/en/v1/manual/performance-tips/#man-performance-column-major) if you are interested in further details."
]
},
{
@@ -315,14 +352,29 @@
},
{
"cell_type": "markdown",
- "id": "9a9e8c44",
+ "id": "5f26f9b5",
"metadata": {},
"source": [
"### Parallelization strategy\n",
"\n",
"As for the matrix-matrix product and Jacobi, any of the iterations over $i$ and $j$ are independent and could be computed on a different processor. However, we need a larger grain size for performance reason. Here, we adopt the same strategy as for algorithm 3 in the matrix-matrix product:\n",
"\n",
- "- Each process will update a subset of consecutive rows of the distance table $C$ at each iteration $k$.\n"
+ "- Each process will update a subset of consecutive rows of the distance table $C$ at each iteration $k$."
+ ]
+ },
+ {
+ "attachments": {
+ "fig-asp-partition.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "9a9e8c44",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "\n",
+ "
"
]
},
{
@@ -336,7 +388,7 @@
"\n",
"`C[i,j] = min(C[i,j],C[i,k]+C[k,j])`\n",
"\n",
- "If each process updates a block of rows of matrix $C$, which data we need for this operation?\n"
+ "If each process updates a block of rows of matrix $C$, which data do we need for this operation?\n"
]
},
{
@@ -423,6 +475,32 @@
"
\n",
+ "Question: How much data is communicated in each iteration in this parallel algorithm?\n",
+ "
\n",
+ "\n",
+ " a) O(N²/P)\n",
+ " b) O(N)\n",
+ " c) O(NP)\n",
+ " d) O(P)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1bf4de56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "answer = \"x\" # replace x with a, b, c or d\n",
+ "floyd_check(answer)"
+ ]
+ },
{
"cell_type": "markdown",
"id": "eba45ea4",
@@ -433,40 +511,68 @@
},
{
"cell_type": "markdown",
- "id": "e96eda2d",
+ "id": "a15bc34e",
"metadata": {},
"source": [
- "- Each process updates $N^2/P$ entries per iteration\n",
- "- 1 process broadcasts a message of length $N$ to $P-1$ processes per iteration\n",
- "- The send cost in this process is $O(N P)$ per iteration (if we use send/receive instead of broadcast)\n",
- "- $P-1$ processes receive one message of length $N$ per iteration\n",
- "- The receive cost is $O(N)$ per iteration at each process\n",
- "- The send/computation ratio is $O(P^2/N)$\n",
- "- The receive/computation ratio is $O(P/N)$\n",
- "- The algorithm is potentially scalable if $P<\n",
- "\n",
+ " \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d89d2b45",
+ "metadata": {},
+ "source": [
+ "**Communication cost:** \n",
+ "- One process broadcasts a message of length $N$ to $P-1$ processes per iteration. Thus, the **send cost** is $O(N P)$ per iteration (if we use send/receive instead of broadcast).\n",
+ "- $P-1$ processes receive one message of length $N$ per iteration. Hence, the **receive cost** is $O(N)$ per iteration at each process. "
+ ]
+ },
+ {
+ "attachments": {
+ "fig-asp-efficiency-comm-2.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "e96eda2d",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "\n",
"
"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "6993b9d0",
+ "metadata": {},
+ "source": [
+ "In summary, the send/computation ratio is $O(P^2/N)$ and the receive/computation ratio is $O(P/N)$. The algorithm is potentially scalable if $P<\n",
+ "Question: Which of the following statements is true?\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4ec6718c",
+ "metadata": {},
+ "source": [
+ " a) The processes are synchronized in each iteration due to the blocking send and receive of row k.\n",
+ " b) Receiving processes may overwrite the data in row k, which can lead to incorrect behavior.\n",
+ " c) The sending process can only continue the computation after the data are received in every other process.\n",
+ " d) The receiving process does not know the source of the received data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4f4a57de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "answer = \"x\" # replace x with a, b, c or d\n",
+ "floyd_impl_check(answer)"
+ ]
+ },
{
"cell_type": "markdown",
"id": "c624722a",
@@ -634,9 +774,8 @@
"source": [
"### Is this implementation correct?\n",
"\n",
- "- Point-to-point messages are *non-overtaking* (i.e. FIFO order) according to section 3.5 of the MPI standard 4.0\n",
- "\n",
- "- Unfortunately this is not enough in this case"
+ "Point-to-point messages are *non-overtaking* (i.e. FIFO order) between the specified sender and receiver according to section 3.5 of the MPI standard 4.0.\n",
+ "Unfortunately this is not enough in this case. The messages can still arrive in the wrong order if messages from different processes overtake each other."
]
},
{
@@ -667,7 +806,7 @@
"id": "df60e4e7",
"metadata": {},
"source": [
- "However, FIFO ordering is not enough. In the next figure communication between process 1 and process 3 is particularly slow. Note that process 3 receives messages from process 1 after the messages received from 2 even though FIFO order is satisfied between any two processors."
+ "However, FIFO ordering is not enough. In the next figure, communication between process 1 and process 3 is particularly slow. Note that process 3 receives messages from process 1 after it receives the messages from 2 even though FIFO ordering is satisfied between any two processors."
]
},
{
@@ -692,10 +831,72 @@
"source": [
"### Possible solutions\n",
"\n",
- "- Use synchronous send MPI_SSEND (less efficient). Note that the blocking send MPI_SEND used above does not guarantee that the message was received.\n",
- "- Barrier at the end of each iteration over $k$ (simple solution, but synchronization overhead)\n",
- "- Order incoming messages (buffering and extra user code needed)\n",
- "- Use a specific rank id instead of `MPI.ANY_SOURCE` or use `MPI.Bcast!` (one needs to know which are the rows owned by the other ranks)"
+ "1. **Synchronous sends**: Use synchronous send MPI_SSEND. This is less efficient because we spend time waiting until each message is received. Note that the blocking send MPI_SEND used above does not guarantee that the message was received. \n",
+ "2. **MPI.Barrier**: Use a barrier at the end of each iteration over $k$. This is easy to implement, but we get a synchronization overhead.\n",
+ "3. **Order incoming messages**: The receiver orders the incoming messages, e.g. according to MPI.Status or the sender rank. This requires buffering and extra user code.\n",
+ "4. **MPI.Bcast!**: Communicate row k using `MPI.Bcast!`. One needs to know which are the rows owned by the other ranks."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "de96ad1b",
+ "metadata": {},
+ "source": [
+ "## Exercise \n",
+ "Rewrite the worker code of the parallel ASP algorithm so it runs correctly. Use the `MPI.Bcast!` to solve the problem of overtaking messages. Note: Only use `MPI.Bcast!`, do not use other MPI directives in addition. You can test your function with the following code cell. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "31194529",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "function floyd_par!(C,N)\n",
+ " comm = MPI.Comm_dup(MPI.COMM_WORLD)\n",
+ " nranks = MPI.Comm_size(comm)\n",
+ " rank = MPI.Comm_rank(comm)\n",
+ " T = eltype(C)\n",
+ " if rank == 0\n",
+ " buffer_root = Vector{T}(undef,N*N)\n",
+ " buffer_root[:] = transpose(C)[:]\n",
+ " else\n",
+ " buffer_root = Vector{T}(undef,0)\n",
+ " end \n",
+ " Nw = div(N,nranks)\n",
+ " buffer = Vector{T}(undef,Nw*N)\n",
+ " MPI.Scatter!(buffer_root,buffer,comm;root=0)\n",
+ " Cw = Matrix{T}(undef,Nw,N)\n",
+ " transpose(Cw)[:] = buffer\n",
+ " MPI.Barrier(comm)\n",
+ " floyd_worker_bcast!(Cw,comm)\n",
+ " buffer[:] = transpose(Cw)[:]\n",
+ " MPI.Gather!(buffer,buffer_root,comm;root=0)\n",
+ " if rank == 0\n",
+ " transpose(C)[:] = buffer_root[:]\n",
+ " end\n",
+ " C\n",
+ "end\n",
+ "\n",
+ "@everywhere function floyd_worker_bcast!(Cw,comm)\n",
+ " # Your implementation here\n",
+ "end\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b7eb4c2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "load = 10\n",
+ "n = nworkers()*load\n",
+ "C = rand_distance_table(n)\n",
+ "C_seq = floyd!(copy(C))\n",
+ "C_par = floyd_par!(copy(C),n)\n",
+ "@test C_seq == C_par"
]
},
{
@@ -713,7 +914,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Julia 1.9.0",
+ "display_name": "Julia 1.9.1",
"language": "julia",
"name": "julia-1.9"
},
@@ -721,7 +922,7 @@
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
- "version": "1.9.0"
+ "version": "1.9.1"
}
},
"nbformat": 4,
diff --git a/dev/asp/index.html b/dev/asp/index.html
index 7f75ffe..88067e9 100644
--- a/dev/asp/index.html
+++ b/dev/asp/index.html
@@ -1,5 +1,5 @@
-All pairs of shortest paths · XM_40017
Compute the length of the shortest path between any two nodes in $G$
-
-
We represent the distance table as a matrix, where $C_{ij}$ is the distance from node $i$ to node $j$. Next figure shows the input and solution (output) of the ASP problem for a simple 4-node directed graph. Note that the minimum distance from node 2 to node 3, which is $C_{23}=8$ as highlighted in the figure.
+
We represent the distance table as a matrix, where $C_{ij}$ is the distance from node $i$ to node $j$. The next figure shows the input and solution (output) of the ASP problem for a simple 4-node directed graph. Note that the minimum distance from node 2 to node 3, $C_{23}=8$, is highlighted in the figure.
This algorithm is memory bound, meaning that the main cost is in getting and setting data from the input matrix C. In this situations, the order in which we traverse the entries of matrix C has a significant performance impact.
This algorithm is memory bound, meaning that the main cost is in getting and setting data from the input matrix C. In this situation, the order in which we traverse the entries of matrix C has a significant performance impact.
The following function computes the same result as for the previous function floyd!, but the nesting of loops over i and j is changed.
@@ -7772,7 +7854,7 @@ a.anchor-link {
-
The performance difference is significant. Matrices in Julia are stored in memory in column-major order (like in Fortran, unlike in C). It means that it is more efficient to access the data also in column-major order (like in function floyd!). See this section of Julia's performance tips if you are interested in further details.
+
The performance difference is significant. Matrices in Julia are stored in memory in column-major order (like in Fortran, unlike in C and Python). It means that it is more efficient to access the data also in column-major order (like in function floyd!). See this section of Julia's performance tips if you are interested in further details.
If each process updates a block of rows of matrix $C$, which data we need for this operation?
+
If each process updates a block of rows of matrix $C$, which data do we need for this operation?
@@ -7914,6 +8009,38 @@ a.anchor-link {
+
+
+
+
+
+
+
+Question: How much data is communicated in each iteration in this parallel algorithm?
+
+
a) O(N²/P)
+b) O(N)
+c) O(NP)
+d) O(P)
+
+
+
+
+
+
+
+
+
In [ ]:
+
+
+
answer="x"# replace x with a, b, c or d
+floyd_check(answer)
+
+
+
+
+
+
@@ -7925,46 +8052,76 @@ a.anchor-link {
-
+
-
-
Each process updates $N^2/P$ entries per iteration
-
1 process broadcasts a message of length $N$ to $P-1$ processes per iteration
-
The send cost in this process is $O(N P)$ per iteration (if we use send/receive instead of broadcast)
-
$P-1$ processes receive one message of length $N$ per iteration
-
The receive cost is $O(N)$ per iteration at each process
-
The send/computation ratio is $O(P^2/N)$
-
The receive/computation ratio is $O(P/N)$
-
The algorithm is potentially scalable if $P<<N$
-
+
Computation cost: Each process updates $N^2/P$ entries per iteration.
-
+
-
+
+
+
+
+
+
+
+
Communication cost:
+
+
One process broadcasts a message of length $N$ to $P-1$ processes per iteration. Thus, the send cost is $O(N P)$ per iteration (if we use send/receive instead of broadcast).
+
$P-1$ processes receive one message of length $N$ per iteration. Hence, the receive cost is $O(N)$ per iteration at each process.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
In summary, the send/computation ratio is $O(P^2/N)$ and the receive/computation ratio is $O(P/N)$. The algorithm is potentially scalable if $P<<N$.
We split the code in two functions. The first function is called on the main process (the process running this notebook). It splits the input matrix into blocks of rows. Then, we call floyd_worker! (see below) remotely on each worker using the corresponding block of rows.
We split the code into two functions. The first function is called on the main process (the process running this notebook). It splits the input matrix into blocks of rows. Then, we use a remotecall to compute Floyd's algorithm in each worker with its corresponding block of rows.
@@ -8056,7 +8213,7 @@ a.anchor-link {
-
The second function is the one run on the workers. Note that we considered MPI for communication in this case.
+
The second function is the one that runs on the workers. Note that we use MPI for communication in this case.
@@ -8076,6 +8233,7 @@ a.anchor-link {
C_k=similar(Cw,n)forkin1:nifkinrows_w
+# Send row k to other workers if I have itmyk=(k-first(rows_w))+1C_k.=view(Cw,myk,:)forprocin0:(nranks-1)
@@ -8085,6 +8243,7 @@ a.anchor-link {
MPI.Send(C_k,comm;dest=proc,tag=0)endelse
+# Wait until row k is receivedMPI.Recv!(C_k,comm,source=MPI.ANY_SOURCE,tag=0)endforjin1:n
@@ -8101,6 +8260,48 @@ a.anchor-link {
+
+
+
+
+
+
+
+Question: Which of the following statements is true?
+
+
+
+
+
+
+
+
+
+
+
+
a) The processes are synchronized in each iteration due to the blocking send and receive of row k.
+b) Receiving processes may overwrite the data in row k, which can lead to incorrect behavior.
+c) The sending process can only continue the computation after the data are received in every other process.
+d) The receiving process does not know the source of the received data.
+
+
+
+
+
+
+
+
+
In [ ]:
+
+
+
answer="x"# replace x with a, b, c or d
+floyd_impl_check(answer)
+
Point-to-point messages are non-overtaking (i.e. FIFO order) between the specified sender and receiver according to section 3.5 of the MPI standard 4.0.
+Unfortunately this is not enough in this case. The messages can still arrive in the wrong order if messages from different processes overtake each other.
@@ -8207,7 +8404,7 @@ a.anchor-link {
-
However, FIFO ordering is not enough. In the next figure communication between process 1 and process 3 is particularly slow. Note that process 3 receives messages from process 1 after the messages received from 2 even though FIFO order is satisfied between any two processors.
+
However, FIFO ordering is not enough. In the next figure, communication between process 1 and process 3 is particularly slow. Note that process 3 receives messages from process 1 after it receives the messages from 2 even though FIFO ordering is satisfied between any two processors.
Synchronous sends: Use synchronous send MPI_SSEND. This is less efficient because we spend time waiting until each message is received. Note that the blocking send MPI_SEND used above does not guarantee that the message was received.
+
MPI.Barrier: Use a barrier at the end of each iteration over $k$. This is easy to implement, but we get a synchronization overhead.
+
Order incoming messages: The receiver orders the incoming messages, e.g. according to MPI.Status or the sender rank. This requires buffering and extra user code.
+
MPI.Bcast!: Communicate row k using MPI.Bcast!. One needs to know which are the rows owned by the other ranks.
Rewrite the worker code of the parallel ASP algorithm so it runs correctly. Use the MPI.Bcast! to solve the problem of overtaking messages. Note: Only use MPI.Bcast!, do not use other MPI directives in addition. You can test your function with the following code cell.
diff --git a/dev/assets/documenter.js b/dev/assets/documenter.js
index f531160..b2bdd43 100644
--- a/dev/assets/documenter.js
+++ b/dev/assets/documenter.js
@@ -4,7 +4,6 @@ requirejs.config({
'highlight-julia': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia.min',
'headroom': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/headroom.min',
'jqueryui': 'https://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.13.2/jquery-ui.min',
- 'minisearch': 'https://cdn.jsdelivr.net/npm/minisearch@6.1.0/dist/umd/index.min',
'katex-auto-render': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/contrib/auto-render.min',
'jquery': 'https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.0/jquery.min',
'headroom-jquery': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/jQuery.headroom.min',
@@ -103,9 +102,10 @@ $(document).on("click", ".docstring header", function () {
});
});
-$(document).on("click", ".docs-article-toggle-button", function () {
+$(document).on("click", ".docs-article-toggle-button", function (event) {
let articleToggleTitle = "Expand docstring";
let navArticleToggleTitle = "Expand all docstrings";
+ let animationSpeed = event.noToggleAnimation ? 0 : 400;
debounce(() => {
if (isExpanded) {
@@ -116,7 +116,7 @@ $(document).on("click", ".docs-article-toggle-button", function () {
isExpanded = false;
- $(".docstring section").slideUp();
+ $(".docstring section").slideUp(animationSpeed);
} else {
$(this).removeClass("fa-chevron-down").addClass("fa-chevron-up");
$(".docstring-article-toggle-button")
@@ -127,7 +127,7 @@ $(document).on("click", ".docs-article-toggle-button", function () {
articleToggleTitle = "Collapse docstring";
navArticleToggleTitle = "Collapse all docstrings";
- $(".docstring section").slideDown();
+ $(".docstring section").slideDown(animationSpeed);
}
$(this).prop("title", navArticleToggleTitle);
@@ -224,224 +224,474 @@ $(document).ready(function () {
})
////////////////////////////////////////////////////////////////////////////////
-require(['jquery', 'minisearch'], function($, minisearch) {
+require(['jquery'], function($) {
-// In general, most search related things will have "search" as a prefix.
-// To get an in-depth about the thought process you can refer: https://hetarth02.hashnode.dev/series/gsoc
+$(document).ready(function () {
+ let meta = $("div[data-docstringscollapsed]").data();
-let results = [];
-let timer = undefined;
-
-let data = documenterSearchIndex["docs"].map((x, key) => {
- x["id"] = key; // minisearch requires a unique for each object
- return x;
+ if (meta?.docstringscollapsed) {
+ $("#documenter-article-toggle-button").trigger({
+ type: "click",
+ noToggleAnimation: true,
+ });
+ }
});
-// list below is the lunr 2.1.3 list minus the intersect with names(Base)
-// (all, any, get, in, is, only, which) and (do, else, for, let, where, while, with)
-// ideally we'd just filter the original list but it's not available as a variable
-const stopWords = new Set([
- "a",
- "able",
- "about",
- "across",
- "after",
- "almost",
- "also",
- "am",
- "among",
- "an",
- "and",
- "are",
- "as",
- "at",
- "be",
- "because",
- "been",
- "but",
- "by",
- "can",
- "cannot",
- "could",
- "dear",
- "did",
- "does",
- "either",
- "ever",
- "every",
- "from",
- "got",
- "had",
- "has",
- "have",
- "he",
- "her",
- "hers",
- "him",
- "his",
- "how",
- "however",
- "i",
- "if",
- "into",
- "it",
- "its",
- "just",
- "least",
- "like",
- "likely",
- "may",
- "me",
- "might",
- "most",
- "must",
- "my",
- "neither",
- "no",
- "nor",
- "not",
- "of",
- "off",
- "often",
- "on",
- "or",
- "other",
- "our",
- "own",
- "rather",
- "said",
- "say",
- "says",
- "she",
- "should",
- "since",
- "so",
- "some",
- "than",
- "that",
- "the",
- "their",
- "them",
- "then",
- "there",
- "these",
- "they",
- "this",
- "tis",
- "to",
- "too",
- "twas",
- "us",
- "wants",
- "was",
- "we",
- "were",
- "what",
- "when",
- "who",
- "whom",
- "why",
- "will",
- "would",
- "yet",
- "you",
- "your",
-]);
+})
+////////////////////////////////////////////////////////////////////////////////
+require(['jquery'], function($) {
-let index = new minisearch({
- fields: ["title", "text"], // fields to index for full-text search
- storeFields: ["location", "title", "text", "category", "page"], // fields to return with search results
- processTerm: (term) => {
- let word = stopWords.has(term) ? null : term;
- if (word) {
- // custom trimmer that doesn't strip @ and !, which are used in julia macro and function names
- word = word
- .replace(/^[^a-zA-Z0-9@!]+/, "")
- .replace(/[^a-zA-Z0-9@!]+$/, "");
- }
+/*
+To get an in-depth about the thought process you can refer: https://hetarth02.hashnode.dev/series/gsoc
- return word ?? null;
- },
- // add . as a separator, because otherwise "title": "Documenter.Anchors.add!", would not find anything if searching for "add!", only for the entire qualification
- tokenize: (string) => string.split(/[\s\-\.]+/),
- // options which will be applied during the search
- searchOptions: {
- boost: { title: 100 },
- fuzzy: 2,
+PSEUDOCODE:
+
+Searching happens automatically as the user types or adjusts the selected filters.
+To preserve responsiveness, as much as possible of the slow parts of the search are done
+in a web worker. Searching and result generation are done in the worker, and filtering and
+DOM updates are done in the main thread. The filters are in the main thread as they should
+be very quick to apply. This lets filters be changed without re-searching with minisearch
+(which is possible even if filtering is on the worker thread) and also lets filters be
+changed _while_ the worker is searching and without message passing (neither of which are
+possible if filtering is on the worker thread)
+
+SEARCH WORKER:
+
+Import minisearch
+
+Build index
+
+On message from main thread
+ run search
+ find the first 200 unique results from each category, and compute their divs for display
+ note that this is necessary and sufficient information for the main thread to find the
+ first 200 unique results from any given filter set
+ post results to main thread
+
+MAIN:
+
+Launch worker
+
+Declare nonconstant globals (worker_is_running, last_search_text, unfiltered_results)
+
+On text update
+ if worker is not running, launch_search()
+
+launch_search
+ set worker_is_running to true, set last_search_text to the search text
+ post the search query to worker
+
+on message from worker
+ if last_search_text is not the same as the text in the search field,
+ the latest search result is not reflective of the latest search query, so update again
+ launch_search()
+ otherwise
+ set worker_is_running to false
+
+ regardless, display the new search results to the user
+ save the unfiltered_results as a global
+ update_search()
+
+on filter click
+ adjust the filter selection
+ update_search()
+
+update_search
+ apply search filters by looping through the unfiltered_results and finding the first 200
+ unique results that match the filters
+
+ Update the DOM
+*/
+
+/////// SEARCH WORKER ///////
+
+function worker_function(documenterSearchIndex, documenterBaseURL, filters) {
+ importScripts(
+ "https://cdn.jsdelivr.net/npm/minisearch@6.1.0/dist/umd/index.min.js"
+ );
+
+ let data = documenterSearchIndex.map((x, key) => {
+ x["id"] = key; // minisearch requires a unique for each object
+ return x;
+ });
+
+ // list below is the lunr 2.1.3 list minus the intersect with names(Base)
+ // (all, any, get, in, is, only, which) and (do, else, for, let, where, while, with)
+ // ideally we'd just filter the original list but it's not available as a variable
+ const stopWords = new Set([
+ "a",
+ "able",
+ "about",
+ "across",
+ "after",
+ "almost",
+ "also",
+ "am",
+ "among",
+ "an",
+ "and",
+ "are",
+ "as",
+ "at",
+ "be",
+ "because",
+ "been",
+ "but",
+ "by",
+ "can",
+ "cannot",
+ "could",
+ "dear",
+ "did",
+ "does",
+ "either",
+ "ever",
+ "every",
+ "from",
+ "got",
+ "had",
+ "has",
+ "have",
+ "he",
+ "her",
+ "hers",
+ "him",
+ "his",
+ "how",
+ "however",
+ "i",
+ "if",
+ "into",
+ "it",
+ "its",
+ "just",
+ "least",
+ "like",
+ "likely",
+ "may",
+ "me",
+ "might",
+ "most",
+ "must",
+ "my",
+ "neither",
+ "no",
+ "nor",
+ "not",
+ "of",
+ "off",
+ "often",
+ "on",
+ "or",
+ "other",
+ "our",
+ "own",
+ "rather",
+ "said",
+ "say",
+ "says",
+ "she",
+ "should",
+ "since",
+ "so",
+ "some",
+ "than",
+ "that",
+ "the",
+ "their",
+ "them",
+ "then",
+ "there",
+ "these",
+ "they",
+ "this",
+ "tis",
+ "to",
+ "too",
+ "twas",
+ "us",
+ "wants",
+ "was",
+ "we",
+ "were",
+ "what",
+ "when",
+ "who",
+ "whom",
+ "why",
+ "will",
+ "would",
+ "yet",
+ "you",
+ "your",
+ ]);
+
+ let index = new MiniSearch({
+ fields: ["title", "text"], // fields to index for full-text search
+ storeFields: ["location", "title", "text", "category", "page"], // fields to return with results
processTerm: (term) => {
let word = stopWords.has(term) ? null : term;
if (word) {
+ // custom trimmer that doesn't strip @ and !, which are used in julia macro and function names
word = word
.replace(/^[^a-zA-Z0-9@!]+/, "")
.replace(/[^a-zA-Z0-9@!]+$/, "");
+
+ word = word.toLowerCase();
}
return word ?? null;
},
+ // add . as a separator, because otherwise "title": "Documenter.Anchors.add!", would not
+ // find anything if searching for "add!", only for the entire qualification
tokenize: (string) => string.split(/[\s\-\.]+/),
- },
+ // options which will be applied during the search
+ searchOptions: {
+ prefix: true,
+ boost: { title: 100 },
+ fuzzy: 2,
+ },
+ });
+
+ index.addAll(data);
+
+ /**
+ * Used to map characters to HTML entities.
+ * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts
+ */
+ const htmlEscapes = {
+ "&": "&",
+ "<": "<",
+ ">": ">",
+ '"': """,
+ "'": "'",
+ };
+
+ /**
+ * Used to match HTML entities and HTML characters.
+ * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts
+ */
+ const reUnescapedHtml = /[&<>"']/g;
+ const reHasUnescapedHtml = RegExp(reUnescapedHtml.source);
+
+ /**
+ * Escape function from lodash
+ * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts
+ */
+ function escape(string) {
+ return string && reHasUnescapedHtml.test(string)
+ ? string.replace(reUnescapedHtml, (chr) => htmlEscapes[chr])
+ : string || "";
+ }
+
+ /**
+ * RegX escape function from MDN
+ * Refer: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+ function escapeRegExp(string) {
+ return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+ }
+
+ /**
+ * Make the result component given a minisearch result data object and the value
+ * of the search input as queryString. To view the result object structure, refer:
+ * https://lucaong.github.io/minisearch/modules/_minisearch_.html#searchresult
+ *
+ * @param {object} result
+ * @param {string} querystring
+ * @returns string
+ */
+ function make_search_result(result, querystring) {
+ let search_divider = ``;
+ let display_link =
+ result.location.slice(Math.max(0), Math.min(50, result.location.length)) +
+ (result.location.length > 30 ? "..." : ""); // To cut-off the link because it messes with the overflow of the whole div
+
+ if (result.page !== "") {
+ display_link += ` (${result.page})`;
+ }
+ searchstring = escapeRegExp(querystring);
+ let textindex = new RegExp(`${searchstring}`, "i").exec(result.text);
+ let text =
+ textindex !== null
+ ? result.text.slice(
+ Math.max(textindex.index - 100, 0),
+ Math.min(
+ textindex.index + querystring.length + 100,
+ result.text.length
+ )
+ )
+ : ""; // cut-off text before and after from the match
+
+ text = text.length ? escape(text) : "";
+
+ let display_result = text.length
+ ? "..." +
+ text.replace(
+ new RegExp(`${escape(searchstring)}`, "i"), // For first occurrence
+ '$&'
+ ) +
+ "..."
+ : ""; // highlights the match
+
+ let in_code = false;
+ if (!["page", "section"].includes(result.category.toLowerCase())) {
+ in_code = true;
+ }
+
+ // We encode the full url to escape some special characters which can lead to broken links
+ let result_div = `
+
+
- `;
-
- return filter_html;
-}
-
-/**
- * Make the result component given a minisearch result data object and the value of the search input as queryString.
- * To view the result object structure, refer: https://lucaong.github.io/minisearch/modules/_minisearch_.html#searchresult
- *
- * @param {object} result
- * @param {string} querystring
- * @returns string
- */
-function make_search_result(result, querystring) {
- let search_divider = ``;
- let display_link =
- result.location.slice(Math.max(0), Math.min(50, result.location.length)) +
- (result.location.length > 30 ? "..." : ""); // To cut-off the link because it messes with the overflow of the whole div
-
- if (result.page !== "") {
- display_link += ` (${result.page})`;
- }
-
- let textindex = new RegExp(`\\b${querystring}\\b`, "i").exec(result.text);
- let text =
- textindex !== null
- ? result.text.slice(
- Math.max(textindex.index - 100, 0),
- Math.min(
- textindex.index + querystring.length + 100,
- result.text.length
- )
- )
- : ""; // cut-off text before and after from the match
-
- let display_result = text.length
- ? "..." +
- text.replace(
- new RegExp(`\\b${querystring}\\b`, "i"), // For first occurrence
- '$&'
- ) +
- "..."
- : ""; // highlights the match
-
- let in_code = false;
- if (!["page", "section"].includes(result.category.toLowerCase())) {
- in_code = true;
- }
-
- // We encode the full url to escape some special characters which can lead to broken links
- let result_div = `
-
-
The programming of this course will be done using the Julia programming language. Thus, we start by explaining how to get up and running with Julia. After studying this page, you will be able to:
Courses related with high-performance computing (HPC) often use languages such as C, C++, or Fortran. We use Julia instead to make the course accessible to a wider set of students, including the ones that have no experience with C/C++ or Fortran, but are willing to learn parallel programming. Julia is a relatively new programming language specifically designed for scientific computing. It combines a high-level syntax close to interpreted languages like Python with the performance of compiled languages like C, C++, or Fortran. Thus, Julia will allow us to write efficient parallel algorithms with a syntax that is convenient in a teaching setting. In addition, Julia provides easy access to different programming models to write distributed algorithms, which will be useful to learn and experiment with them.
Tip
You can run the code in this link to learn how Julia compares to other languages (C and Python) in terms of performance.
There are several ways of opening Julia depending on your operating system and your IDE, but it is usually as simple as launching the Julia app. With VSCode, open a folder (File > Open Folder). Then, press Ctrl+Shift+P to open the command bar, and execute Julia: Start REPL. If this does not work, make sure you have the Julia extension for VSCode installed. Independently of the method you use, opening Julia results in a window with some text ending with:
julia>
You have just opened the Julia read-evaluate-print loop, or simply the Julia REPL. Congrats! You will spend most of time using the REPL, when working in Julia. The REPL is a console waiting for user input. Just as in other consoles, the string of text right before the input area (julia> in the case) is called the command prompt or simply the prompt.
Curious about what the function println does? Enter into help mode to look into the documentation. This is done by typing a question mark (?) into the input field:
julia> ?
After typing ?, the command prompt changes to help?>. It means we are in help mode. Now, we can type a function name to see its documentation.
The REPL comes with two more modes, namely package and shell modes. To enter package mode type
julia> ]
Package mode is used to install and manage packages. We are going to discuss the package mode in greater detail later. To return back to normal mode press the backspace key several times.
To enter shell mode type semicolon (;)
julia> ;
The prompt should have changed to shell> indicating that we are in shell mode. Now you can type commands that you would normally do on your system command line. For instance,
shell> ls
will display the contents of the current folder in Mac or Linux. Using shell mode in Windows is not straightforward, and thus not recommended for beginners.
Real-world Julia programs are not typed in the REPL in practice. They are written in one or more files and included in the REPL. To try this, create a new file called hello.jl, write the code of the "Hello world" example above, and save it. If you are using VSCode, you can create the file using File > New File > Julia File. Once the file is saved with the name hello.jl, execute it as follows
julia> include("hello.jl")
Warning
Make sure that the file "hello.jl" is located in the current working directory of your Julia session. You can query the current directory with function pwd(). You can change to another directory with function cd() if needed. Also, make sure that the file extension is .jl.
The recommended way of running Julia code is using the REPL as we did. But it is also possible to run code directly from the system command line. To this end, open a terminal and call Julia followed by the path to the file containing the code you want to execute.
$ julia hello.jl
The previous line assumes that you have Julia properly installed in the system and that it's usable from the terminal. In UNIX systems (Linux and Mac), the Julia binary needs to be in one of the directories listed in the PATH environment variable. To check that Julia is properly installed, you can use
$ julia --version
If this runs without error and you see a version number, you are good to go!
Note
In this tutorial, when a code snipped starts with $, it should be run in the terminal. Otherwise, the code is to be run in the Julia REPL.
Tip
Avoid calling Julia code from the terminal, use the Julia REPL instead! Each time you call Julia from the terminal, you start a fresh Julia session and Julia will need to compile your code from scratch. This can be time consuming for large projects. In contrast, if you execute code in the REPL, Julia will compile code incrementally, which is much faster. Running code in a cluster (like in DAS-5 for the Julia assignment) is among the few situations you need to run Julia code from the terminal.
The programming of this course will be done using the Julia programming language. Thus, we start by explaining how to get up and running with Julia. After studying this page, you will be able to:
Courses related with high-performance computing (HPC) often use languages such as C, C++, or Fortran. We use Julia instead to make the course accessible to a wider set of students, including the ones that have no experience with C/C++ or Fortran, but are willing to learn parallel programming. Julia is a relatively new programming language specifically designed for scientific computing. It combines a high-level syntax close to interpreted languages like Python with the performance of compiled languages like C, C++, or Fortran. Thus, Julia will allow us to write efficient parallel algorithms with a syntax that is convenient in a teaching setting. In addition, Julia provides easy access to different programming models to write distributed algorithms, which will be useful to learn and experiment with them.
Tip
You can run the code in this link to learn how Julia compares to other languages (C and Python) in terms of performance.
There are several ways of opening Julia depending on your operating system and your IDE, but it is usually as simple as launching the Julia app. With VSCode, open a folder (File > Open Folder). Then, press Ctrl+Shift+P to open the command bar, and execute Julia: Start REPL. If this does not work, make sure you have the Julia extension for VSCode installed. Independently of the method you use, opening Julia results in a window with some text ending with:
julia>
You have just opened the Julia read-evaluate-print loop, or simply the Julia REPL. Congrats! You will spend most of time using the REPL, when working in Julia. The REPL is a console waiting for user input. Just as in other consoles, the string of text right before the input area (julia> in the case) is called the command prompt or simply the prompt.
Curious about what the function println does? Enter into help mode to look into the documentation. This is done by typing a question mark (?) into the input field:
julia> ?
After typing ?, the command prompt changes to help?>. It means we are in help mode. Now, we can type a function name to see its documentation.
The REPL comes with two more modes, namely package and shell modes. To enter package mode type
julia> ]
Package mode is used to install and manage packages. We are going to discuss the package mode in greater detail later. To return back to normal mode press the backspace key several times.
To enter shell mode type semicolon (;)
julia> ;
The prompt should have changed to shell> indicating that we are in shell mode. Now you can type commands that you would normally do on your system command line. For instance,
shell> ls
will display the contents of the current folder in Mac or Linux. Using shell mode in Windows is not straightforward, and thus not recommended for beginners.
Real-world Julia programs are not typed in the REPL in practice. They are written in one or more files and included in the REPL. To try this, create a new file called hello.jl, write the code of the "Hello world" example above, and save it. If you are using VSCode, you can create the file using File > New File > Julia File. Once the file is saved with the name hello.jl, execute it as follows
julia> include("hello.jl")
Warning
Make sure that the file "hello.jl" is located in the current working directory of your Julia session. You can query the current directory with function pwd(). You can change to another directory with function cd() if needed. Also, make sure that the file extension is .jl.
The recommended way of running Julia code is using the REPL as we did. But it is also possible to run code directly from the system command line. To this end, open a terminal and call Julia followed by the path to the file containing the code you want to execute.
$ julia hello.jl
The previous line assumes that you have Julia properly installed in the system and that it's usable from the terminal. In UNIX systems (Linux and Mac), the Julia binary needs to be in one of the directories listed in the PATH environment variable. To check that Julia is properly installed, you can use
$ julia --version
If this runs without error and you see a version number, you are good to go!
Note
In this tutorial, when a code snipped starts with $, it should be run in the terminal. Otherwise, the code is to be run in the Julia REPL.
Tip
Avoid calling Julia code from the terminal, use the Julia REPL instead! Each time you call Julia from the terminal, you start a fresh Julia session and Julia will need to compile your code from scratch. This can be time consuming for large projects. In contrast, if you execute code in the REPL, Julia will compile code incrementally, which is much faster. Running code in a cluster (like in DAS-5 for the Julia assignment) is among the few situations you need to run Julia code from the terminal. Visit this link (Julia workflow tips) from the official Julia documentation for further information about how to develop Julia code effectivelly.
Since we are in a parallel computing course, let's run a parallel "Hello world" example in Julia. Open a Julia REPL and write
julia> using Distributed
julia> @everywhere println("Hello, world! I am proc $(myid()) from $(nprocs())")
Here, we are using the Distributed package, which is part of the Julia standard library that provides distributed memory parallel support. The code prints the process id and the number of processes in the current Julia session.
You will probably only see output from 1 process. We need to add more processes to run the example in parallel. This is done with the addprocs function.
julia> addprocs(3)
We have added 3 new processes. Plus the old one, we have 4 processes. Run the code again.
julia> @everywhere println("Hello, world! I am proc $(myid()) from $(nprocs())")
Now, you should see output from 4 processes.
It is possible to specify the number of processes when starting Julia from the terminal with the -p argument (useful, e.g., when running in a cluster). If you launch Julia from the terminal as
$ julia -p 3
and then run
julia> @everywhere println("Hello, world! I am proc $(myid()) from $(nprocs())")
One of the most useful features of Julia is its package manager. It allows one to install Julia packages in a straightforward and platform independent way. To illustrate this, let us consider the following parallel "Hello world" example. This example uses the Message Passing Interface (MPI). We will learn more about MPI later in the course.
Copy the following block of code into a new file named "hello_mpi.jl"
# file hello_mpi.jl
using MPI
MPI.Init()
comm = MPI.COMM_WORLD
rank = MPI.Comm_rank(comm)
nranks = MPI.Comm_size(comm)
-println("Hello world, I am rank $rank of $nranks")
As you can see from this example, one can access MPI from Julia in a clean way, without type annotations and other complexities of C/C++ code.
Now, run the file from the REPL
julia> include("hello_mpi.jl")
It probably didn't work, right? Read the error message and note that the MPI package needs to be installed to run this code.
To install a package, we need to enter package mode. Remember that we entered into help mode by typing ?. Package mode is activated by typing ] :
julia> ]
At this point, the prompt should have changed to (@v1.8) pkg> indicating that we are in package mode. The text between the parentheses indicates which is the active project, i.e., where packages are going to be installed. In this case, we are working with the global project associated with our Julia installation (which is Julia 1.8 in this example, but it can be another version in your case).
To install the MPI package, type
(@v1.8) pkg> add MPI
Congrats, you have installed MPI!
Note
Many Julia package names end with .jl. This is just a way of signaling that a package is written in Julia. When using such packages, the .jl needs to be omitted. In this case, we have installed the MPI.jl package even though we have only typed MPI in the REPL.
Note
The package you have installed is the Julia interface to MPI, called MPI.jl. Note that it is not a MPI library by itself. It is just a thin wrapper between MPI and Julia. To use this interface, you need an actual MPI library installed in your system such as OpenMPI or MPICH. Julia downloads and installs a MPI library for you, but it is also possible to use a MPI library already available in your system. This is useful, e.g., when running on HPC clusters. See the documentation of MPI.jl for further details.
To check that the package was installed properly, exit package mode by pressing the backspace key several times, and run it again
julia> include("hello_mpi.jl")
Now, it should work, but you probably get output from a single MPI rank only.
To run MPI applications in parallel, you need a launcher like mpiexec. MPI codes written in Julia are not an exception to this rule. From the system terminal, you can run
$ mpiexec -np 4 julia hello_mpi.jl
But it will probably not work since the version of mpiexec needs to match with the MPI version we are using from Julia. Don't worry if you could not make it work! A more elegant way to run MPI code is from the Julia REPL directly, by using these commands:
julia> using MPI
-julia> mpiexec(cmd->run(`$cmd -np 4 julia hello_mpi.jl`))
We have installed the MPI package globally and it will be available in all Julia sessions. However, in some situations, we want to work with different versions of the same package or to install packages in an isolated way to avoid potential conflicts with other packages. This can be done by using local projects.
A project is simply a folder in the hard disk. To use a particular folder as your project, you need to activate it. This is done by entering package mode and using the activate command followed by the path to the folder you want to activate.
(@v1.8) pkg> activate .
The previous command will activate the current working directory. Note that the dot . is indeed the path to the current folder.
The prompt has changed to (lessons) pkg> indicating that we are in the project within the lessons folder. The particular folder name can be different in your case.
Tip
You can activate a project directly when opening Julia from the terminal using the --project flag. The command $ julia --project=. will open Julia and activate a project in the current directory. You can also achieve the same effect by setting the environment variable JULIA_PROJECT with the path of the folder you want to activate.
Note
The active project folder and the current working directory are two independent concepts! For instance, (@v1.8) pkg> activate folderB and then julia> cd("folderA"), will activate the project in folderB and change the current working directory to folderA.
At this point all package-related operations will be local to the new project. For instance, install the DataFrames package.
(lessons) pkg> add DataFrames
Use the package to check that it is installed
julia> using DataFrames
-julia> DataFrame(a=[1,2],b=[3,4])
Now, we can return to the global project to check that DataFrames has not been installed there. To return to the global environment, use activate without a folder name.
(lessons) pkg> activate
The prompt is again (@v1.8) pkg>
Now, try to use DataFrames.
julia> using DataFrames
-julia> DataFrame(a=[1,2],b=[3,4])
You should get an error or a warning unless you already had DataFrames installed globally.
The information about a project is stored in two files Project.toml and Manifest.toml.
Project.toml contains the packages explicitly installed (the direct dependencies)
Manifest.toml contains direct and indirect dependencies along with the concrete version of each package.
In other words, Project.toml contains the packages relevant for the user, whereas Manifest.toml is the detailed snapshot of all dependencies. The Manifest.toml can be used to reproduce the same environment in another machine.
You can see the path to the current Project.toml file by using the status operator (or st in its short form) while in package mode
(@v1.8) pkg> status
The information about the Manifest.toml can be inspected by passing the -m flag.
Project files can be used to install lists of packages defined by others. E.g., to install all the dependencies of a Julia application.
Assume that a colleague has sent to you a Project.toml file with this content:
[deps]
+println("Hello world, I am rank $rank of $nranks")
As you can see from this example, one can access MPI from Julia in a clean way, without type annotations and other complexities of C/C++ code.
Now, run the file from the REPL
julia> include("hello_mpi.jl")
It probably didn't work, right? Read the error message and note that the MPI package needs to be installed to run this code.
To install a package, we need to enter package mode. Remember that we entered into help mode by typing ?. Package mode is activated by typing ] :
julia> ]
At this point, the prompt should have changed to (@v1.10) pkg> indicating that we are in package mode. The text between the parentheses indicates which is the active project, i.e., where packages are going to be installed. In this case, we are working with the global project associated with our Julia installation (which is Julia 1.10 in this example, but it can be another version in your case).
To install the MPI package, type
(@v1.10) pkg> add MPI
Congrats, you have installed MPI!
Note
Many Julia package names end with .jl. This is just a way of signaling that a package is written in Julia. When using such packages, the .jl needs to be omitted. In this case, we have installed the MPI.jl package even though we have only typed MPI in the REPL.
Note
The package you have installed is the Julia interface to MPI, called MPI.jl. Note that it is not a MPI library by itself. It is just a thin wrapper between MPI and Julia. To use this interface, you need an actual MPI library installed in your system such as OpenMPI or MPICH. Julia downloads and installs a MPI library for you, but it is also possible to use a MPI library already available in your system. This is useful, e.g., when running on HPC clusters. See the documentation of MPI.jl for further details.
To check that the package was installed properly, exit package mode by pressing the backspace key several times, and run it again
julia> include("hello_mpi.jl")
Now, it should work, but you probably get output from a single MPI rank only.
To run MPI applications in parallel, you need a launcher like mpiexec. MPI codes written in Julia are not an exception to this rule. From the system terminal, you can run
$ mpiexec -np 4 julia hello_mpi.jl
But it will probably not work since the version of mpiexec needs to match with the MPI version we are using from Julia. Don't worry if you could not make it work! A more elegant way to run MPI code is from the Julia REPL directly, by using these commands:
julia> using MPI
+julia> run(`$(mpiexec()) -np 4 julia hello_mpi.jl`)
We have installed the MPI package globally and it will be available in all Julia sessions. However, in some situations, we want to work with different versions of the same package or to install packages in an isolated way to avoid potential conflicts with other packages. This can be done by using local projects.
A project is simply a folder in your file system. To use a particular folder as your project, you need to activate it. This is done by entering package mode and using the activate command followed by the path to the folder you want to activate.
(@v1.10) pkg> activate .
The previous command will activate the current working directory. Note that the dot . is indeed the path to the current folder.
The prompt has changed to (lessons) pkg> indicating that we are in the project within the lessons folder. The particular folder name can be different in your case.
Tip
You can activate a project directly when opening Julia from the terminal using the --project flag. The command $ julia --project=. will open Julia and activate a project in the current directory. You can also achieve the same effect by setting the environment variable JULIA_PROJECT with the path of the folder you want to activate.
Note
The active project folder and the current working directory are two independent concepts! For instance, (@v1.10) pkg> activate folderB and then julia> cd("folderA"), will activate the project in folderB and change the current working directory to folderA.
At this point all package-related operations will be local to the new project. For instance, install the DataFrames package.
(lessons) pkg> add DataFrames
Use the package to check that it is installed
julia> using DataFrames
+julia> DataFrame(a=[1,2],b=[3,4])
Now, we can return to the global project to check that DataFrames has not been installed there. To return to the global environment, use activate without a folder name.
(lessons) pkg> activate
The prompt is again (@v1.10) pkg>
Now, try to use DataFrames.
julia> using DataFrames
+julia> DataFrame(a=[1,2],b=[3,4])
You should get an error or a warning unless you already had DataFrames installed globally.
The information about a project is stored in two files Project.toml and Manifest.toml.
Project.toml contains the packages explicitly installed (the direct dependencies)
Manifest.toml contains direct and indirect dependencies along with the concrete version of each package.
In other words, Project.toml contains the packages relevant for the user, whereas Manifest.toml is the detailed snapshot of all dependencies. The Manifest.toml can be used to reproduce the same environment in another machine.
You can see the path to the current Project.toml file by using the status operator (or st in its short form) while in package mode
(@v1.10) pkg> status
The information about the Manifest.toml can be inspected by passing the -m flag.
Copy the contents of previous code block into a file called Project.toml and place it in an empty folder named newproject. It is important that the file is named Project.toml. You can create a new folder from the REPL with
julia> mkdir("newproject")
To install all the packages registered in this file you need to activate the folder containing your Project.toml file
(@v1.8) pkg> activate newproject
and then instantiating it
(newproject) pkg> instantiate
The instantiate command will download and install all listed packages and their dependencies in just one click.
In some situations it is required to use package commands in Julia code, e.g., to automatize installation and deployment of Julia applications. This can be done using the Pkg package. For instance
We have learned the basics of how to work with Julia. If you want to further dig into the topics we have covered here, you can take a look at the following links:
This document was generated with Documenter.jl version 1.1.1 on Monday 16 October 2023. Using Julia version 1.9.3.
+MPI = "da04e1cc-30fd-572f-bb4f-1f8673147195"
Copy the contents of previous code block into a file called Project.toml and place it in an empty folder named newproject. It is important that the file is named Project.toml. You can create a new folder from the REPL with
julia> mkdir("newproject")
To install all the packages registered in this file you need to activate the folder containing your Project.toml file
(@v1.10) pkg> activate newproject
and then instantiating it
(newproject) pkg> instantiate
The instantiate command will download and install all listed packages and their dependencies in just one click.
In some situations it is required to use package commands in Julia code, e.g., to automatize installation and deployment of Julia applications. This can be done using the Pkg package. For instance
We have learned the basics of how to work with Julia. If you want to further dig into the topics we have covered here, you can take a look at the following links:
This page contains part of the course material of the Programming Large-Scale Parallel Systems course at VU Amsterdam. We provide several lecture notes in jupyter notebook format, which will help you to learn how to design, analyze, and program parallel algorithms on multi-node computing systems. Further information about the course is found in the study guide (click here) and our Canvas page (for registered students).
Note
Material will be added incrementally to the website as the course advances.
Warning
This page will eventually contain only a part of the course material. The rest will be available on Canvas. In particular, the material in this public webpage does not fully cover all topics in the final exam.
Download the notebooks and run them locally on your computer (recommended). At each notebook page you will find a green box with links to download the notebook.
You also have the static version of the notebooks displayed in this webpage for quick reference.
This page contains part of the course material of the Programming Large-Scale Parallel Systems course at VU Amsterdam. We provide several lecture notes in jupyter notebook format, which will help you to learn how to design, analyze, and program parallel algorithms on multi-node computing systems. Further information about the course is found in the study guide (click here) and our Canvas page (for registered students).
Note
Material will be added incrementally to the website as the course advances.
Warning
This page will eventually contain only a part of the course material. The rest will be available on Canvas. In particular, the material in this public webpage does not fully cover all topics in the final exam.
Download the notebooks and run them locally on your computer (recommended). At each notebook page you will find a green box with links to download the notebook.
You also have the static version of the notebooks displayed in this webpage for quick reference.
This page was created with the support of the Faculty of Science of Vrije Universiteit Amsterdam in the framework of the project "Interactive lecture notes and exercises for the Programming Large-Scale Parallel Systems course" funded by the "Innovation budget BETA 2023 Studievoorschotmiddelen (SVM) towards Activated Blended Learning".
Settings
This document was generated with Documenter.jl version 1.1.1 on Monday 16 October 2023. Using Julia version 1.9.3.
+julia> notebook()
These commands will open a jupyter in your web browser. Navigate in jupyter to the notebook file you have downloaded and open it.
This page was created with the support of the Faculty of Science of Vrije Universiteit Amsterdam in the framework of the project "Interactive lecture notes and exercises for the Programming Large-Scale Parallel Systems course" funded by the "Innovation budget BETA 2023 Studievoorschotmiddelen (SVM) towards Activated Blended Learning".
Settings
This document was generated with Documenter.jl version 1.5.0 on Monday 19 August 2024. Using Julia version 1.10.4.
This document was generated with Documenter.jl version 1.1.1 on Monday 16 October 2023. Using Julia version 1.9.3.
+
Settings
This document was generated with Documenter.jl version 1.5.0 on Monday 19 August 2024. Using Julia version 1.10.4.
diff --git a/dev/jacobi_2D_src/index.html b/dev/jacobi_2D_src/index.html
index 9dd80d0..c3e73cd 100644
--- a/dev/jacobi_2D_src/index.html
+++ b/dev/jacobi_2D_src/index.html
@@ -7333,11 +7333,12 @@ a.anchor-link {
if (!diagrams.length) {
return;
}
- const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.5.0/mermaid.esm.min.mjs")).default;
+ const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.7.0/mermaid.esm.min.mjs")).default;
const parser = new DOMParser();
mermaid.initialize({
maxTextSize: 100000,
+ maxEdges: 100000,
startOnLoad: false,
fontFamily: window
.getComputedStyle(document.body)
@@ -7408,7 +7409,8 @@ a.anchor-link {
let results = null;
let output = null;
try {
- const { svg } = await mermaid.render(id, raw, el);
+ let { svg } = await mermaid.render(id, raw, el);
+ svg = cleanMermaidSvg(svg);
results = makeMermaidImage(svg);
output = document.createElement("figure");
results.map(output.appendChild, output);
@@ -7423,6 +7425,38 @@ a.anchor-link {
parent.appendChild(output);
}
+
+ /**
+ * Post-process to ensure mermaid diagrams contain only valid SVG and XHTML.
+ */
+ function cleanMermaidSvg(svg) {
+ return svg.replace(RE_VOID_ELEMENT, replaceVoidElement);
+ }
+
+
+ /**
+ * A regular expression for all void elements, which may include attributes and
+ * a slash.
+ *
+ * @see https://developer.mozilla.org/en-US/docs/Glossary/Void_element
+ *
+ * Of these, only ` ` is generated by Mermaid in place of `\n`,
+ * but _any_ "malformed" tag will break the SVG rendering entirely.
+ */
+ const RE_VOID_ELEMENT =
+ /<\s*(area|base|br|col|embed|hr|img|input|link|meta|param|source|track|wbr)\s*([^>]*?)\s*>/gi;
+
+ /**
+ * Ensure a void element is closed with a slash, preserving any attributes.
+ */
+ function replaceVoidElement(match, tag, rest) {
+ rest = rest.trim();
+ if (!rest.endsWith('/')) {
+ rest = `${rest} /`;
+ }
+ return `<${tag} ${rest}>`;
+ }
+
void Promise.all([...diagrams].map(renderOneMarmaid));
});
diff --git a/dev/jacobi_method.ipynb b/dev/jacobi_method.ipynb
index c2e519f..af2f3dc 100644
--- a/dev/jacobi_method.ipynb
+++ b/dev/jacobi_method.ipynb
@@ -39,7 +39,7 @@
"metadata": {},
"source": [
"
\n",
- "Note: Do not forget to run the next cell before starting studying this notebook. \n",
+ "Note: Do not forget to run the next cell before you start studying this notebook. \n",
"
"
]
},
@@ -614,10 +614,10 @@
"Question: At the end of function jacobi_mpi ...\n",
"
\n",
"\n",
- " a) each rank holds the complete solution.\n",
- " b) only the root process holds the solution. \n",
- " c) the values of the ghost cells of u are not consistent with the neighbors\n",
- " d) the ghost cells of u contain the initial values -1 and 1 in all ranks"
+ " a) each process holds the complete solution.\n",
+ " b) the complete solution is gathered in the root process. \n",
+ " c) each process contains the solution for the local partition. \n",
+ " d) the ghost cells of u contain the initial values -1 and 1 in all processes."
]
},
{
@@ -772,7 +772,7 @@
},
{
"cell_type": "markdown",
- "id": "267ecd2a",
+ "id": "f93e2024",
"metadata": {},
"source": [
"### Parallelization strategies\n",
@@ -783,21 +783,21 @@
"- 2D block partition (each worker handles a subset of consecutive rows and columns)\n",
"- 2D cyclic partition (each workers handles a subset of alternating rows ans columns)\n",
"\n",
- "The three partition types are depicted in the following figure for 4 processes.\n"
+ "The three partition types are depicted in the following figure for 4 processes."
]
},
{
"attachments": {
- "fig18.png": {
- "image/png": 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"
+ "fig-jacobi-partitions.png": {
+ "image/png": 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0mf0kMjNZqX1VmQi7y91faOP9Yp/1f0XsDNEB26ryXOH1elyXdSeFOSGFpJBwiCUwCnM3wMy+oDALwOmqvVj6JyU9bWY7d1Vc1STFBW9VmDGhWkIpplFhusfnzGzfDsawipndpXDXu62EUtqmCvVU3jKzn3UkjraY2f6S/qTKA+cjkj7VWvdXAF0uNtta0aZxPknxoeiFaMMsOEVhkoxjVfuNtcMkPZtM7VxXSQLsUYW6DdUSSjEDJH1ToQ3bqoMxbKDQM2GC2k4oleqlcMFxkaTJSY/yLmNmX1M8oXSDpEOSwr0A2mBmeyn8vadnTpsqaXd3f6v+UbVLrN2sx7lsbNaymifp6KAbVDmJxiiFDgqtiR2fL6zh/fL8rPWyPHzGDiOpVCDuPkfxP+CPm1mutTMk9TezaxTuWqzUjucPkvTPjp7cZpEUnLtHHethM1jSjWZ2ZDtj2FzSfyTt2oEYJKmfurAobysnEA8rTAdajzs7AKqLXcy/Ue8gWpMUI782sulAM8t7xtaVFaZEPk/tmz12pKTbkxlg6iIZivygKqd3zmIVSXebWdYZgJbG8CmFdiDd9T+rwYrPDtQpzOxYSedHNl0v6VASSkBtzGw3hcREn9SmmZL2Sqa47y5ix+tqM293pmGRdXXp6Zoc6y6PbKpasDtpa9JtxBsK5UDaEvusXVI+JEex695cei4XGcPfiuf3ClMoNqTW76N4AbZ6WV3SEZH1SyS9rFB/Yo7CuNqNFa/Js4KkCWa2obvP76pAJcnM+ihcQMROxmcpqTmhkM2fo1ATYrxCJn986vGNkq4ysxnu/s8MMWwt6W7Fu982K6k5kcSwQOFOwliFA3vditsmFxs3qLIGxUMKJxBdPmQBQHXJ0KMdI5syT9VbB5ersi5Df4XE+s31D6fF1lXWL1S4yHhLYVrfkQoJlH6Rx46RdK2ZfaKr6wSa2TCF9iN2h3SqpL9Lul+h/VikEPdWkj6rygLugyTdbGbbZRmGmAx/uU7xc8XFyfs/rFDLqUmhDfuYwvC3LL2qOsTMvqgwO5WlNl0n6XB3b6pXLEB3lkwN/w9VHv/mKNxg/F/9o2ofMxur+LVIPWrkxRIt9TyXvkzSd1R+o3gnM9uoSo3AE1V5/LyoxiFssc/a025Ex3q89bTP2GEklQrG3aea2X8lbZPatKvyTSqlPS7pCkl/d/cppRvMrJ9CXY1fKtwlLbWGwoHuB10c308VTyj9QdIp6ZgTf5f0IzP7nMJ3XXpSbJKuMLPx7p7uVloh6SX1D8UTShMkne7ub7Ty/I0V6ip9RfGLm07RygnEg5L2JqEEFML2ig/fvb3egdTgfwpJhnThzryTSml3KAyLvjndEzMZNn2EpLNVeVGyncLQ6Ku6OL7LVJlQcoXhXT9OejanXW9mZ0g6TmFik0El2/pK+qOZbVFLLRQzW19heur0eWJTEtuZ1WpMJZNibK1QB+oQVd4k6zRmdpik36my5/1fJX2ehBJQGzPbT+H8tH9q03RJ+7j7o/WPqkN2jqxbrDCUt6ulEzRSOH7Xhbu/Y2Z/V6iDV+qrSg1zM7OBCm1aqXkKnRxqketnrZPl4TN2GMPfiumByLosdQy60kSFZMPW7n5ZLDnj7gvc/S8KPX5i3UxPMbPBXRWgmW2pyoLnknS2ux9ZJaHUwt3/qjA7XDp5NFy1J/YuUOXMTM2STnD3Q1tLKCUxPOPuJypcSF6mLjh4mdnHFS7y0kNBHhA9lIAi+U5k3WsKyd9CSXrwxOIqSht2r6SPu/se7v6n2NBed5/t7hdL2lLxbvw/ShInXSKZMOGz6bAkHefup1ZJKIUHuS9JYt9boXdBqfUUaje29f6mcEGRbhsWSDrQ3U9srWi5uze7+6Pu/vnkPf/e1nu2h5l9VtI1qkxaTRAJJaBmZvYVhR7r6YTSe5J2dPdH6h9Vh8VqCN1XpxkgY/UOY72mutLFkXVHJDdNSn1e0pDUumvdfXqN7xP7rF12jZeT5eEzdhhJpWJ6ObJujaQHUF6aFHr/bO3ut9XyhOSk8wBVTunZX5UnzJ3pm6rct2939zNqfQF3f0nhQJtO5hzYVn2rpI5SbJaZM9z9klpjSOJ4z92PU6gLNTnLc1uT1La6TWFIYqn7FZKGVS9aANSPme2jeD21X3T1EKwOeCmyru5FrlPmKfSc2bXWO+7u/orCzKFpYxUfjthZYoXNL3X3WJ2MKHd/QNLXI5uOjVxUpB2oMNtN2pHuflOtMSRxTHL3AxR6ftV6kdKmpFdFbKbSP0v6AgkloDZmdrrCyIP039IkSdtXGS5VaGY2RmE2s7S/1SmE2M2I9tSjbTd3v1eVQ/1WkPSF1Lr2FuheKvfPWgfLw2fsMJJKxfRmZF2DQqHRvExy9++3MR1lBXd/TmFGsbTDOyescmY2WtJBqdXNih80W+Xu9yh0oS/VS+HCpDUnqbKr5KOKz0pTayy3u/t+7X1+KTPbVKHeVDrLfp9IKAGFYWajFO+C/pLCRUBRxWYGSg+Hq7d73f2irIk4d79Vob5cWle1YVtJ+nhq9QzFe6u15UpV1t0aLOnLbTzvpMi6Ce5+XTtikCS5+7XuflR7n1/KzPZUqJeUnljiT5KOIKEEtM3MepnZrxWG+abPWSdK2sHdX69/ZJ3ix6pMkn2k+EQSXSHWkzNdr7UeYr2VTlj6g5l9QtImqe33ZEwkxj5r+jW7u6L8PguNpFIxVbuoH1RlfdFdE1m3nZl1RZ2F/VU5Y8W97j6pna8Xu3BLj1FukXym/SObfl5jwbsuZWbrKiSU0oX1HpC0r7vPrX9UANKSyQb+ospkTLOkYwo+m1WsDeuTfKbuKNaGfaKL3ivWvlzfniEbSQLtd5FN6RsvLcxshKQdIpvOzfr+XcHMdlCY0S09scQESV8sQjsLFJ2Z9VWomRbrzfigpJ3d/f36RtU5kslnvhTZdEkdb5o+E1k3Pvne6+kPqiwovVFST1XqeC8lKf5Zt+iia7y8xD7jx8wsz84ehUNSqZiqzYwWK/rcHdyvyouM/pLW7oL3St/hlTrW3fVuVWaoVzGzarOzbarKsclTVYACtUlC6T5Vzspzt8KsHvRQAgqgpKbNTpHNP0yGNhVZtULQ3fXGyK2RdeuYWbrmUGfo7DbsOlUO496ylQTfTqrstfCEuz/ZgRg6RZJQuk2V50J/EDWUgJokF8J3Sjo0svnvkvaoU92hTpec5/5Jlde370j6eR1DeUrx657YTecuk5zXx26KfDXpCX1gav2bkmqe5TrxuMIMpKWGSvpUxtcpsjcU9qFSJulz9Q+luEgqFVO12kltzthSRMmQuRcim7qi6+B2kXXtnunB3ZslxU6mY+8jxS8CH827V4GZrSXpLlUmlO6StF8tswEB6HpJQulChZpuaTco1LYrunSx16W6ZU9Id39blTUVGtTJxceTRM+WkU0dacOmKxR1L9Vf0mZVnhKrFZV7EtPMtpV0iyoTStdI+jI9lIC2mdk6kh5RvKflLyV91t2r3dgutGSSnnsVJtVJ+0ZsUoau4u4LJf07sumr9YqhxMWK1IeV9H1Vjuy4JOuxNPle74lsyuOzdomk128s2XZcD+uR1SEklYqpWhHNmXWNonPFZoFLJzg6JJmNJz3ttquyUF1WsW6P1XpZrRdZ998Ovn+HmNk4heTRmNSmO0VCCSiaXyjeJf0+hQLERS3OXSrWhi1ITrS7q9gEGp3ahklaTZU3lT5oa8bSGsTasHWqPHb9yLq827AtFJ9Y4mqRUAJqYmY7KSSU1kptapJ0vLufktxI7VbMrJ+ZnaZQ+y4967IUEiXX1zksSTo/sm5nM4vdMKqJmTWa2alZnuPuLyiMSCjVRyW1lRLz1f5ajbFh1vua2Wfa+Xoysz5mdkp7n98FLlQoP1BqE8VrENbEgq93Ua/nuiOpVEyxoVWLFIZRdVex2Dt7es3Bqtyn53fCsK7YCX26JlFr69/u4Pu3m5mtqpBQGpvadIekT3fXO1JAT2RmZyk+89ejCgng7vL3GmvD3q17FJ0rVqizs9uw9NBpqXPa/VgbNrTKY4vWho2XdLsqv5srJX2lO14EA/VmZkcq/B2l/75nStrH3S+tf1QdY2YrmtkJCrPUnaPKXjdS6EETqxvV5ZJh6rFenhclPS8zSSbZuV/ST9oRzm9qeMyf3D02y1ktblR81tfLzaxar9iqkgkrHlb7JqjoEkly7sbIprPMLPNQv6TX4L8l/VqVk050SySViinWC+alvIdQdVAssdPZJ+Sxk+TZnfC6sS6zWU7I69blNmW0QkIp3XtLkn7TjS5QgR7PzL4v6YzIpqcUZmXsjGNZvcTasFhvme6ENqz+NlC4AZKOqUnShSSUgLaZ2YqSrlJl0uUNSdu7++31jikrM+trZuub2T5m9m0zu1Mh4f4bVfbCX+pfChPQ5Hnt9D1V9m4ZIukOM/tKMsKiVWa2lpldLul/itfcq8VNkia38ZiL2vnaSurZfVOVw+xWlnS3mX0+GdrfKjNbz8yukfSYpC3aG08X+pEq60f1l/TPpMdRm8khM1stmXXxWUm7d0GMuUlPt4hiiI11fqruUXSuWAHNNg8wGcW6D3bGcIsFkXXViqbH1ueVvLlK8TvfkvRHM9vd3R+pYzwAIszsxwr1DdImKhRNnV7nkNotOXHcPrKJNqxttGHl/qF4G9Yo6VYz26EDM7sCywtT/Fg1UOFiuDPf62B3b08NuP+ZWTr50kchxkHK1pPDFYaenZbUdM2Nu99vZueo8obRIIUhY980s78o9KiarHDzYqDCEMUtJe2rMBtnhzqBuHuTmV2q6jUZH3D3DrXR7n6bmV2gyp5hQyRdK+lkM/urQt2rdxQ+6yCFm1BbSdpPoV5tZ7erncbdn0mGH/46talPsu7E5Pd5p6S3FHoCDpA0TiFJtq+kXdRD8y898kN1Z2a2kuKFOm+rdyydLFZjo73dLKuJ3U3tjBnzYjMWVZsZI1b3qlqNrK5WLaEkhe/lFjP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SARjinAIYNlU6wFPcdOkAADVxsw4AAABAZ0PvdLzzl5LTDi4VZbxXfznZvrcdX3hCsvZpRePs563fTG5/uB2vXJa868yyeUZ96u7ko3f0559ZUy7LOLtnk1d8qT9/15ntdazJq65Jdsy047UnJheeWDbPqD+4IVn/SDs+7eD2eVyTj92RfPLudjzZJJ86v2yeUbtnkldcM/RPd5VJwmOYGZz8pzOTVbXtEV9OdsydU68+MXlVZXvEW76ZfG/unFq1rL2GNfnkXcnHftSOmySfruyc2rG3fYz3ec/rklOOLRZnrFe+P9m5ux2/dnXyynPK5hn1W1ckm7e349NPSN65tmyeUZd/Mvna99vx1GTyqbeUzTNq5+72MR6woVAUWkPX/3+dl8yr7DPOv/HF/viPX5WcWdnrp7V/kmyd+2L7vzk+ed1JRePs5x03JbdsasfPPCZ532+XzTPqszcm//UL/fmnzm/vsWsyuAYvW5ucdkK5LOO8/N3J7NyPANXYMbz+umTTrnb87BXJ5b9VNs+om9cn7/h4fz5UKr3gsGTNUU90pMc3PfBZqpMPSn69shu593+3Pz54fn359r2Q2Ke2fPsKw33OOjxZfWSZLI9laiKPvqx9+pL6ruHlN/dLpUMrXIPfebBfKjVNfflG12CSRwrE4Bf0wsOS85aXTjFsukl2zI1rPKfee0t/fEiFe8StDw3Pa8u3Zffw/KxnJueeUibLY5kauFd6xtHJrz+3XJZxFs7vl0qHLakv31XX9Uuliaa+fFv3/1W5HWP+jCfO9sHJBcckCyv+QuLzn55cUNkbjvMGrtdJi+vb9z94W3+8bFF9e8L6+4bnLztm+DVzbV7wjGTNaaVTDJto+qVSjfduB073S6WDD6xvDR4wb3he8fIDAAAAoFZKJQAAAAA6UyoBAAAA0JlSCQAAAIDOlEoAAAAAdKZUAgAAAKAzpRIAAAAAnSmVAAAAAOhMqQQAAABAZ0olAAAAADpTKgEAAADQmVIJAAAAgM6USgAAAAB0plQCAAAAoDOlEgAAAACdKZUAAAAA6EypBAAAAEBnSiUAAAAAOlMqAQAAANCZUgkAAACAzpRKAAAAAHSmVAIAAACgM6USAAAAAJ0plQAAAADoTKkEAAAAQGdKJQAAAAA6UyoBAAAA0JlSCQAAAIDOlEoAAAAAdKZUAgAAAKAzpRIAAAAAnSmVAAAAAOhMqQQAAABAZ0olAAAAADpTKgEAAADQWZOkt29y0HQyXVnN9OCu/njeRLJ4ulyWcR7enczMXcGJJlk2r2yeUdv2Jjtn+vND5pfLMk4vyaaBx/jAqWT+ZLE4Y9W+Bh/anczOrcHpifZ5XJMn2xpM8pper/fxMmkY1TTNwiTb9s2nmqRpCgYaY89sf9wkmarsHN07O3DQp75zfqbX38OS+vIlw4/x5EQyUVnGPXv746ZJpio7R2vPNzMzsganymUZq5fsmRn6lz/v9Xq/XyjNU17TNB9O8u/3zWvfsyYm2n2rJkN7Qp4E52Zle8LMbDI78Bhbg90NrsGJJpms+d6ywnOzN9s+T/YZeops2fNEx+lm9+zwC/zazPbqzpfUn2/r3va/WtW+BvdUni+pP1+SxaUDMGToNmRvL8N3epXpZfhGoEby/f+ZmW3/q1WvN3yzXJva8yX150uytHSAp7ih61/7njU7UkDU5klxbla+J9R+/Wpfg7Mjb27V5slwblbWGQIAAADwZDD0SaVXnZscd1ipKON98LPJrrlPUJ31zOTcU8rmGfU31yb3PtiOjz00WfvLZfOMuvGO5Jrv9ucX/+tyWcbZO5P8yWf687W/3F7Hmlzx98nuuXb4nGclZz+rbJ5RV34hefCRdnzcYe3zuCZfvCX51o/a8USTXPSbZfOMGl2DSX5WKArjDb038+oTkxWLSkUZ74rbk91zX005+/DknCPK5hn11+uSn8x9gXDFovYa1uTr9yfXbuzPL15VLss4u2eTK27rz1+zOjnmkHJ5xvnTv++/i3nuKe39Uk0Gz9HjD08uPKdsnlGfuC655/52PDmRvPnlZfOM2jOT/OnwOXVvoSi0fjw4WfWiP8zEZF3fj7r5/7730fEFi0/JiullBdPs768euiG7eu2mcPhxL8jyk1YXTjTszpuuytaH7k6SHDG1OC8/6NTCiYbdunNDrt++7tH5qS++KE1T12dFBtfgry1emaOnlxRMs7+PbLr+0fERJ5yVI0+s6wXUrdd+MDN72693LJ86KP/qoLpujn6yZ3P+4ZFbH50P7YBvWJOsOe0Jz/S4/vJz/VLpxacml60tm2fU9d/vl0onHJG853Vl84x6398Nl0q15du+e/gF/e++NFn97HJ5xvnP/9S/GX7J6cklF5bNM+ra25IHf9COT1pe32N82dUDpdJEffm279qvVLqvUBTGG/rA9O88PTlveako4/3F9/ul0vlHJZeeUTbPqOvu65dKJy5OLn9O2TyjLr+lXyo1qS/flt3DpdKbXlrfG1wf/sd+qfQrpyd/9MqyeUZ97Jpkw0Pt+OSj6jsHfvxA8vGBUqm2fFt37lcqrS8UhdbQ9T/zgndmat7CUlnGGnxB/28POiOrF51UMM3+rtp8U3bNtJvW8pNW57m/9p7CiYbdf883Hi2VjppakosOXVM40bCPPnzDUKn0nF99VyYm6/pR1cE1+IolZ+TshXW9o3Xlpq9lZu4W86iTX5wzL7iscKJhd970iWx96J4kyTHTS6tbg9dvXzdUKtVVaQIAAADwpKBUAgAAAKAzpRIAAAAAnSmVAAAAAOhMqQQAAABAZ0olAAAAADpTKgEAAADQmVIJAAAAgM6USgAAAAB0plQCAAAAoDOlEgAAAACdKZUAAAAA6EypBAAAAEBnSiUAAAAAOlMqAQAAANCZUgkAAACAzpRKAAAAAHSmVAIAAACgM6USAAAAAJ0plQAAAADoTKkEAAAAQGdKJQAAAAA6UyoBAAAA0JlSCQAAAIDOlEoAAAAAdKZUAgAAAKAzpRIAAAAAnSmVAAAAAOhMqQQAAABAZ0olAAAAADpTKgEAAADQmVIJAAAAgM6USgAAAAB0plQCAAAAoDOlEgAAAACdNUl6+yann5AsO7BgmjG+clsyM9uOj1yaPOvYsnlGfeOOZOvOdrxwfvL8p5fNM+rOjck99/fn560ql2Wc2V5y7a39+coVyWFLyuUZ59pb25xJsvzg5JlHl80z6oYfJtt3teNFC5LnnVw2z6gfbUh+/EA7bprkRSvL5hk1ugaT/Gav1/t0oTiMaJpmfpKd++ZnHJIsnVcw0BjXbuzvEUcekDxradk8o268P9m2tx0vmkqed1jZPKN+tCX58bZ23CR50fKicfazt5dct7E/r/2cOurg5BmVnVPXfS/ZO7cGD1yQPLeyc+q7dycPbGnHE02yurJzama2vR8ecFGv1/tAoThPeU3TvCXJ5fvmy5+2Ok1T1/v0P/3RNY+OT5x3aA6fqusF3jd23J2ZXrtpHbD48Cw74tmFEw27/95vZs/OR5IkC5rpnH5AXZvqvXs25949Dz06X/60F6VpmoKJ9je4Bk+ad1gOnVpUMM3+bth+d3pzNcjCxUdm6RHPKpxo2Mb112d2ZneS5KDJBTll/pGFEw3bPLMz39vVvzkaKpUAyO/1er2PlA5Bq2maA5JsL50DoCKX93q9t5UO8VTVNM37klxUOgdALeqq1QEAAAB4UpganKxckSyp65Np+foP+l9/O/bQZEVlH9v/7t3Jlrn30A9amKw6rmyeUT95MLnrZ/352XV9si+zs8nXftCfrzquvY41GVyDKw5r12FNbrkreWRHO16yMFlZ2Rq8c0Oy8eF23DTJWc8sm2fU6BpMsq1QFMabHZysXJYsmS4VZbyv35/MzH3m99hFyYrKztFbHkoe2dOOl8xLVlb29bx7tyd3b+3Pzz68XJZxZnrtY7yPc6q7m+9Kts6dU0sXJc9eUTTOfr53b7Kp/abLk+Wc2lQoCq2h6//Cw+t7l/76gXv/U46t7+dNbvhhsnemHR+zKDmusnPz1oeTze03j7L4gOTU44vG2c+GTcm6+/rzsw5vv35Uk8E1WH3HUOG9240PJHvm8tXYMWzeltx6T38+VCpd8YZkzWlPdKTHt3RtsnmutPntFyeXrS2bZ9S5b0u+ens7Pv2E5Np3lc0z6n1/l1z80f78q5c/9t+WsH13suiV/fmHfjdZXdfXqnPQq/ulzRvWJJdcWDbPqBde3G6MSfKck5IvvrNsnlGXXZ1c8vF2PDlR4RrclSwafkzXFYrCeHsHJ3/2/OS8yn5zZ+lfJ1vmSpvXn5xcekbZPKPO+T/Jv8zd3J1xcPKlC8rmGXX5Lcnbv9WOmyTXvaxonP1s2Z0s/Zv+/MNvSs49pVyecRa/qv/7jm98SfJHr3z8v3+iPf+i5MY72vFzT04+f2nROPu59KrkP17Vjqcn6zuntu5sH+MBXy0UhdZXBidfeGmycOqx/rSMif/RH3/g9ckFv1QuyziHvDbZNPdmwmtOTC5/Ttk8o87/XHLNhna8ckV9e8Kf/0Py+1f259dckExX1mwOrsHqO4aTkndW9hxZ/U/933M87fjkK+8uGmc/X7w5eckf9+eVLT8AAAAAngyUSgAAAAB0plQCAAAAoDOlEgAAAACdKZUAAAAA6EypBAAAAEBnSiUAAAAAOlMqAQAAANCZUgkAAACAzpRKAAAAAHSmVAIAAACgM6USAAAAAJ0plQAAAADoTKkEAAAAQGdKJQAAAAA6UyoBAAAA0JlSCQAAAIDOlEoAAAAAdKZUAgAAAKAzpRIAAAAAnSmVAAAAAOhMqQQAAABAZ0olAAAAADpTKgEAAADQmVIJAAAAgM6USgAAAAB0plQCAAAAoDOlEgAAAACdKZUAAAAA6EypBAAAAEBnSiUAAAAAOlMqAQAAANCZUgkAAACAzpRKAAAAAHSmVAIAAACgs6nByXfWJxNNqSjj7Z3tj++6L/nSLeWyjLN5W3/88Lb68t25YXheW75de4bn316XzMyUyfJYZgbW4PoK1+CWgTX4UIVrcN3G/rjXqy/frr2lE9DFtzclvdIhRuwdCHTX1uRLGx77b0vYMrDPPry7vnzrHhme15Zv+8gecdOdyZ7K9o3Bc2pdjefUjv540yP15bvrvv64xnNq556f/zeUc+3GZP5k6RSP7eb1yfypn/93T6Q9A/f692yrb99/eHd/vGVHfXvCHSPX68sbk8nKXsMPqr5jqPDebfOugfH2+tbgd9YPz5vUd38OUNLZvV7vX0qHoNU0zWSSyl7CAxT1wl6v9/XSIZ6qmqY5K8n1pXMA1MLX3wAAAADoTKkEAAAAQGf/D1klUiTLIaWrAAAAAElFTkSuQmCC"
}
},
"cell_type": "markdown",
- "id": "e52959a5",
+ "id": "267ecd2a",
"metadata": {},
"source": [
"
\n",
- "\n",
+ "\n",
"
"
]
},
@@ -937,7 +937,7 @@
"\n",
"\n",
"- Both 1d and 2d block partitions are potentially scalable if $P<
-Jacobi method · XM_40017
This document was generated with Documenter.jl version 1.1.1 on Monday 16 October 2023. Using Julia version 1.9.3.
+
Settings
This document was generated with Documenter.jl version 1.5.0 on Monday 19 August 2024. Using Julia version 1.10.4.
diff --git a/dev/jacobi_method_src/index.html b/dev/jacobi_method_src/index.html
index 251c46b..7b8be6a 100644
--- a/dev/jacobi_method_src/index.html
+++ b/dev/jacobi_method_src/index.html
@@ -7333,11 +7333,12 @@ a.anchor-link {
if (!diagrams.length) {
return;
}
- const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.5.0/mermaid.esm.min.mjs")).default;
+ const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.7.0/mermaid.esm.min.mjs")).default;
const parser = new DOMParser();
mermaid.initialize({
maxTextSize: 100000,
+ maxEdges: 100000,
startOnLoad: false,
fontFamily: window
.getComputedStyle(document.body)
@@ -7408,7 +7409,8 @@ a.anchor-link {
let results = null;
let output = null;
try {
- const { svg } = await mermaid.render(id, raw, el);
+ let { svg } = await mermaid.render(id, raw, el);
+ svg = cleanMermaidSvg(svg);
results = makeMermaidImage(svg);
output = document.createElement("figure");
results.map(output.appendChild, output);
@@ -7423,6 +7425,38 @@ a.anchor-link {
parent.appendChild(output);
}
+
+ /**
+ * Post-process to ensure mermaid diagrams contain only valid SVG and XHTML.
+ */
+ function cleanMermaidSvg(svg) {
+ return svg.replace(RE_VOID_ELEMENT, replaceVoidElement);
+ }
+
+
+ /**
+ * A regular expression for all void elements, which may include attributes and
+ * a slash.
+ *
+ * @see https://developer.mozilla.org/en-US/docs/Glossary/Void_element
+ *
+ * Of these, only ` ` is generated by Mermaid in place of `\n`,
+ * but _any_ "malformed" tag will break the SVG rendering entirely.
+ */
+ const RE_VOID_ELEMENT =
+ /<\s*(area|base|br|col|embed|hr|img|input|link|meta|param|source|track|wbr)\s*([^>]*?)\s*>/gi;
+
+ /**
+ * Ensure a void element is closed with a slash, preserving any attributes.
+ */
+ function replaceVoidElement(match, tag, rest) {
+ rest = rest.trim();
+ if (!rest.endsWith('/')) {
+ rest = `${rest} /`;
+ }
+ return `<${tag} ${rest}>`;
+ }
+
void Promise.all([...diagrams].map(renderOneMarmaid));
});
@@ -7524,7 +7558,7 @@ a.anchor-link {
-Note: Do not forget to run the next cell before starting studying this notebook.
+Note: Do not forget to run the next cell before you start studying this notebook.
@@ -8169,10 +8203,10 @@ d) 4
Question: At the end of function jacobi_mpi ...
-
a) each rank holds the complete solution.
-b) only the root process holds the solution.
-c) the values of the ghost cells of u are not consistent with the neighbors
-d) the ghost cells of u contain the initial values -1 and 1 in all ranks
+
a) each process holds the complete solution.
+b) the complete solution is gathered in the root process.
+c) each process contains the solution for the local partition.
+d) the ghost cells of u contain the initial values -1 and 1 in all processes.
@@ -8337,7 +8371,7 @@ d) the ghost cells of u contain the initial values -1 and 1 in all ranks<
-
+
@@ -8354,14 +8388,14 @@ d) the ghost cells of u contain the initial values -1 and 1 in all ranks<
-
+
-
+
@@ -8550,7 +8584,7 @@ d) the ghost cells of u contain the initial values -1 and 1 in all ranks<
Both 1d and 2d block partitions are potentially scalable if $P<<N$
-
The 2d block partition is with the lowest communication complexity
+
The 2d block partition has the lowest communication complexity
The 1d block partition requires to send less messages (It can be useful if the fixed cost of sending a message is high)
The best strategy for a given problem size will thus depend on the machine, but the 2d block partition is the one used in practice since it has the lowest communication complexity.
Cyclic partitions are impractical for this application (but they are useful in others)
diff --git a/dev/julia_async.ipynb b/dev/julia_async.ipynb
index d18e354..0862493 100644
--- a/dev/julia_async.ipynb
+++ b/dev/julia_async.ipynb
@@ -28,6 +28,56 @@
"Understanding these concepts is important to learn distributed computing later."
]
},
+ {
+ "cell_type": "markdown",
+ "id": "cde5ee75",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Note: Do not forget to execute the next cell before starting this notebook! \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0b0496c7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "function why_q1()\n",
+ " msg = \"\"\"\n",
+ " Evaluating compute_π(100_000_000) takes about 0.25 seconds on the teacher's laptop. Thus, the loop would take about 2.5 seconds since we are calling the function 10 times.\n",
+ " \"\"\"\n",
+ " println(msg)\n",
+ "end\n",
+ "function why_q2()\n",
+ " msg = \"\"\"\n",
+ " The time in doing the loop will be almost zero since the loop just schedules 10 tasks, which should be very fast.\n",
+ " \"\"\"\n",
+ " println(msg)\n",
+ "end\n",
+ "function why_q3()\n",
+ " msg = \"\"\"\n",
+ " It will take 2.5 seconds, like in question 1. The @sync macro forces to wait for all tasks we have generated with the @async macro. Since we have created 10 tasks and each of them takes about 0.25 seconds, the total time will be about 2.5 seconds.\n",
+ " \"\"\"\n",
+ " println(msg)\n",
+ "end\n",
+ "function why_q4()\n",
+ " msg = \"\"\"\n",
+ " It will take about 3 seconds. The channel has buffer size 4, thus the call to put!will not block. The call to take! will not block neither since there is a value stored in the channel. The taken value is 3 and therefore we will wait for 3 seconds.\n",
+ " \"\"\"\n",
+ " println(msg)\n",
+ "end\n",
+ "function why_q5()\n",
+ " msg = \"\"\"\n",
+ " The channel is not buffered and therefore the call to put! will block. The cell will run forever, since there is no other task that calls take! on this channel.\n",
+ " \"\"\"\n",
+ " println(msg)\n",
+ "end\n",
+ "println(\"🥳 Well done! \")"
+ ]
+ },
{
"cell_type": "markdown",
"id": "caf64254",
@@ -37,7 +87,7 @@
"\n",
"### Creating a task\n",
"\n",
- "Technically, a task in Julia is a *symmetric co-routine*. More informally, a task is a piece of computation work that can be started (scheduled) at some point in the future, and that can be interrupted and resumed. To create a task, we first need to create a function that represents the work to be done in the task. In next cell, we generate a task that generates and sums two matrices."
+ "Technically, a task in Julia is a *symmetric* [*co-routine*](https://en.wikipedia.org/wiki/Coroutine). More informally, a task is a piece of computational work that can be started (scheduled) at some point in the future, and that can be interrupted and resumed. To create a task, we first need to create a function that represents the work to be done in the task. In next cell, we generate a task that generates and sums two matrices."
]
},
{
@@ -726,6 +776,16 @@
"end"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6b8382e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "why_q1()"
+ ]
+ },
{
"cell_type": "markdown",
"id": "5f19d38c",
@@ -754,6 +814,16 @@
"end"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "edff9747",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "why_q2()"
+ ]
+ },
{
"cell_type": "markdown",
"id": "5041c355",
@@ -781,6 +851,16 @@
"end"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "87bc7c5c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "why_q3()"
+ ]
+ },
{
"cell_type": "markdown",
"id": "841b690e",
@@ -821,6 +901,16 @@
"end"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a18a0a7d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "why_q4()"
+ ]
+ },
{
"cell_type": "markdown",
"id": "df663f11",
@@ -860,6 +950,26 @@
"end"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d8923fae",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "why_q5()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0ee77abe",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Note: If for some reason a cell keeps running forever, we can stop it with Kernel > Interrupt or Kernel > Restart (see tabs above).\n",
+ "
This document was generated with Documenter.jl version 1.1.1 on Monday 16 October 2023. Using Julia version 1.9.3.
+
Settings
This document was generated with Documenter.jl version 1.5.0 on Monday 19 August 2024. Using Julia version 1.10.4.
diff --git a/dev/julia_async_src/index.html b/dev/julia_async_src/index.html
index 29a1804..97cd238 100644
--- a/dev/julia_async_src/index.html
+++ b/dev/julia_async_src/index.html
@@ -7333,11 +7333,12 @@ a.anchor-link {
if (!diagrams.length) {
return;
}
- const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.5.0/mermaid.esm.min.mjs")).default;
+ const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.7.0/mermaid.esm.min.mjs")).default;
const parser = new DOMParser();
mermaid.initialize({
maxTextSize: 100000,
+ maxEdges: 100000,
startOnLoad: false,
fontFamily: window
.getComputedStyle(document.body)
@@ -7408,7 +7409,8 @@ a.anchor-link {
let results = null;
let output = null;
try {
- const { svg } = await mermaid.render(id, raw, el);
+ let { svg } = await mermaid.render(id, raw, el);
+ svg = cleanMermaidSvg(svg);
results = makeMermaidImage(svg);
output = document.createElement("figure");
results.map(output.appendChild, output);
@@ -7423,6 +7425,38 @@ a.anchor-link {
parent.appendChild(output);
}
+
+ /**
+ * Post-process to ensure mermaid diagrams contain only valid SVG and XHTML.
+ */
+ function cleanMermaidSvg(svg) {
+ return svg.replace(RE_VOID_ELEMENT, replaceVoidElement);
+ }
+
+
+ /**
+ * A regular expression for all void elements, which may include attributes and
+ * a slash.
+ *
+ * @see https://developer.mozilla.org/en-US/docs/Glossary/Void_element
+ *
+ * Of these, only ` ` is generated by Mermaid in place of `\n`,
+ * but _any_ "malformed" tag will break the SVG rendering entirely.
+ */
+ const RE_VOID_ELEMENT =
+ /<\s*(area|base|br|col|embed|hr|img|input|link|meta|param|source|track|wbr)\s*([^>]*?)\s*>/gi;
+
+ /**
+ * Ensure a void element is closed with a slash, preserving any attributes.
+ */
+ function replaceVoidElement(match, tag, rest) {
+ rest = rest.trim();
+ if (!rest.endsWith('/')) {
+ rest = `${rest} /`;
+ }
+ return `<${tag} ${rest}>`;
+ }
+
void Promise.all([...diagrams].map(renderOneMarmaid));
});
@@ -7506,13 +7540,70 @@ a.anchor-link {
+
+
+
+
+
+
+
+Note: Do not forget to execute the next cell before starting this notebook!
+
+
+
+
+
+
+
+
+
+
In [ ]:
+
+
+
functionwhy_q1()
+msg="""
+ Evaluating compute_π(100_000_000) takes about 0.25 seconds on the teacher's laptop. Thus, the loop would take about 2.5 seconds since we are calling the function 10 times.
+ """
+println(msg)
+end
+functionwhy_q2()
+msg="""
+ The time in doing the loop will be almost zero since the loop just schedules 10 tasks, which should be very fast.
+ """
+println(msg)
+end
+functionwhy_q3()
+msg="""
+ It will take 2.5 seconds, like in question 1. The @sync macro forces to wait for all tasks we have generated with the @async macro. Since we have created 10 tasks and each of them takes about 0.25 seconds, the total time will be about 2.5 seconds.
+ """
+println(msg)
+end
+functionwhy_q4()
+msg="""
+ It will take about 3 seconds. The channel has buffer size 4, thus the call to put!will not block. The call to take! will not block neither since there is a value stored in the channel. The taken value is 3 and therefore we will wait for 3 seconds.
+ """
+println(msg)
+end
+functionwhy_q5()
+msg="""
+ The channel is not buffered and therefore the call to put! will block. The cell will run forever, since there is no other task that calls take! on this channel.
+ """
+println(msg)
+end
+println("🥳 Well done! ")
+
Technically, a task in Julia is a symmetric co-routine. More informally, a task is a piece of computation work that can be started (scheduled) at some point in the future, and that can be interrupted and resumed. To create a task, we first need to create a function that represents the work to be done in the task. In next cell, we generate a task that generates and sums two matrices.
Technically, a task in Julia is a symmetricco-routine. More informally, a task is a piece of computational work that can be started (scheduled) at some point in the future, and that can be interrupted and resumed. To create a task, we first need to create a function that represents the work to be done in the task. In next cell, we generate a task that generates and sums two matrices.
@@ -8397,6 +8488,20 @@ d) near 0*t
+
+
+
+
+
+
In [ ]:
+
+
+
why_q1()
+
+
+
+
+
@@ -8430,6 +8535,20 @@ d) near 0*t
+
+
+
+
+
+
In [ ]:
+
+
+
why_q2()
+
+
+
+
+
@@ -8463,6 +8582,20 @@ d) near 0*t
+
+
+
+
+
+
In [ ]:
+
+
+
why_q3()
+
+
+
+
+
@@ -8513,6 +8646,20 @@ d) 3 seconds
+
+
+
+
+
+
In [ ]:
+
+
+
why_q4()
+
+
+
+
+
@@ -8562,6 +8709,33 @@ d) 3 seconds
+
+
+
+
+
+
In [ ]:
+
+
+
why_q5()
+
+
+
+
+
+
+
+
+
+
+
+
+
+Note: If for some reason a cell keeps running forever, we can stop it with Kernel > Interrupt or Kernel > Restart (see tabs above).
+
+
+
+
diff --git a/dev/julia_basics.ipynb b/dev/julia_basics.ipynb
index ff73688..85e87d4 100644
--- a/dev/julia_basics.ipynb
+++ b/dev/julia_basics.ipynb
@@ -147,6 +147,44 @@
"foo()"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "d18e679d",
+ "metadata": {},
+ "source": [
+ "### A very easy first exercise\n",
+ "\n",
+ "Run the following cell. It contains definitions used later in the notebook."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "81678b3d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "function why_q1()\n",
+ " msg = \"\"\"\n",
+ " In the first line, we assign a variable to a value. In the second line, we assign another variable to the same value. Thus, we have 2 variables associated with the same value. In line 3, we associate y to a new value (re-assignment). Thus, we have 2 variables associated with 2 different values. Variable x is still associated with its original value. Thus, the value at the final line is x=1.\n",
+ " \"\"\"\n",
+ " println(msg)\n",
+ "end\n",
+ "function why_q2()\n",
+ " msg = \"\"\"\n",
+ " It will be 1 for very similar reasons as in the previous questions: we are reassigning a local variable, not the global variable defined outside the function.\n",
+ " \"\"\"\n",
+ " println(msg)\n",
+ "end\n",
+ "function why_q3()\n",
+ " msg = \"\"\"\n",
+ " It will be 6. In the returned function f2, x is equal to 2. Thus, when calling f2(3) we compute 2*3.\n",
+ " \"\"\"\n",
+ " println(msg)\n",
+ "end\n",
+ "println(\"🥳 Well done! \")"
+ ]
+ },
{
"cell_type": "markdown",
"id": "92112bd1",
@@ -467,6 +505,24 @@
"x"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "a2f94960",
+ "metadata": {},
+ "source": [
+ "Run next cell to get an explanation of this question."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc562337",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "why_q1()"
+ ]
+ },
{
"cell_type": "markdown",
"id": "4d2cb752",
@@ -586,6 +642,24 @@
"x"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "f69108c2",
+ "metadata": {},
+ "source": [
+ "Run next cell to get an explanation of this question."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "05c62aa3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "why_q2()"
+ ]
+ },
{
"cell_type": "markdown",
"id": "4fc5eb9b",
@@ -1068,6 +1142,24 @@
"x"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "062ff145",
+ "metadata": {},
+ "source": [
+ "Run next cell to get an explanation of this question."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6bf7818e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "why_q3()"
+ ]
+ },
{
"cell_type": "markdown",
"id": "bc8e9bcf",
@@ -1649,15 +1741,15 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Julia 1.9.0",
+ "display_name": "Julia 1.10.0",
"language": "julia",
- "name": "julia-1.9"
+ "name": "julia-1.10"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
- "version": "1.9.0"
+ "version": "1.10.0"
}
},
"nbformat": 4,
diff --git a/dev/julia_basics/index.html b/dev/julia_basics/index.html
index 2c223c3..82d1463 100644
--- a/dev/julia_basics/index.html
+++ b/dev/julia_basics/index.html
@@ -1,5 +1,5 @@
-Julia Basics · XM_40017
Run the following cell. It contains definitions used later in the notebook.
+
+
+
+
+
+
+
+
+
In [ ]:
+
+
+
functionwhy_q1()
+msg="""
+ In the first line, we assign a variable to a value. In the second line, we assign another variable to the same value. Thus, we have 2 variables associated with the same value. In line 3, we associate y to a new value (re-assignment). Thus, we have 2 variables associated with 2 different values. Variable x is still associated with its original value. Thus, the value at the final line is x=1.
+ """
+println(msg)
+end
+functionwhy_q2()
+msg="""
+ It will be 1 for very similar reasons as in the previous questions: we are reassigning a local variable, not the global variable defined outside the function.
+ """
+println(msg)
+end
+functionwhy_q3()
+msg="""
+ It will be 6. In the returned function f2, x is equal to 2. Thus, when calling f2(3) we compute 2*3.
+ """
+println(msg)
+end
+println("🥳 Well done! ")
+
+
+
+
+
+
@@ -8049,6 +8126,31 @@ a.anchor-link {
+
+
+
+
+
+
+
Run next cell to get an explanation of this question.
+
+
+
+
+
+
+
+
+
In [ ]:
+
+
+
why_q1()
+
+
+
+
+
+
@@ -8200,6 +8302,31 @@ a.anchor-link {
+
+
+
+
+
+
+
Run next cell to get an explanation of this question.
+
+
+
+
+
+
+
+
+
In [ ]:
+
+
+
why_q2()
+
+
+
+
+
+
@@ -8825,6 +8952,31 @@ a.anchor-link {
+
+
+
+
+
+
+
Run next cell to get an explanation of this question.
+
+
+
+
+
+
+
+
+
In [ ]:
+
+
+
why_q3()
+
+
+
+
+
+
diff --git a/dev/julia_distributed.ipynb b/dev/julia_distributed.ipynb
index 5dea45e..7dd1269 100644
--- a/dev/julia_distributed.ipynb
+++ b/dev/julia_distributed.ipynb
@@ -25,7 +25,7 @@
"source": [
"## Contents\n",
"\n",
- "In this notebook, we will learn the basics of distributed computing in Julia. In particular, we will focus on the Distributed module available in the Julia standard library. The main topics we are going to cover are:\n",
+ "In this notebook, we will learn the basics of distributed computing in Julia. In particular, we will focus on the [Distributed](https://docs.julialang.org/en/v1/manual/distributed-computing/) module available in the Julia standard library. The main topics we are going to cover are:\n",
"\n",
"- How to create Julia processes\n",
"- How to execute code remotely\n",
@@ -60,7 +60,32 @@
" end |> println\n",
"end\n",
"q_1_check(answer) = answer_checker(answer,\"a\")\n",
- "q_2_check(answer) = answer_checker(answer,\"b\")"
+ "q_2_check(answer) = answer_checker(answer,\"b\")\n",
+ "function why_q1()\n",
+ " msg = \"\"\"\n",
+ " We send the matrix (16 entries) and then we receive back the result (1 extra integer). Thus, the total number of transferred integers in 17.\n",
+ " \"\"\"\n",
+ " display(msg)\n",
+ "end\n",
+ "function why_q2()\n",
+ " msg = \"\"\"\n",
+ " Even though we only use a single entry of the matrix in the remote worker, the entire matrix is captured and sent to the worker. Thus, we will transfer 17 integers like in Question 1.\n",
+ " \"\"\"\n",
+ " display(msg)\n",
+ "end\n",
+ "function why_q3()\n",
+ " msg = \"\"\"\n",
+ " The value of x will still be zero since the worker receives a copy of the matrix and it modifies this copy, not the original one.\n",
+ " \"\"\"\n",
+ " display(msg)\n",
+ "end\n",
+ "function why_q4()\n",
+ " msg = \"\"\"\n",
+ " In this case, the code a[2]=2 is executed in the main process. Since the matrix is already in the main process, it is not needed to create and send a copy of it. Thus, the code modifies the original matrix and the value of x will be 2.\n",
+ " \"\"\"\n",
+ " display(msg)\n",
+ "end\n",
+ "println(\"🥳 Well done! \")"
]
},
{
@@ -621,8 +646,6 @@
"source": [
"### This will not work!\n",
"\n",
- "You really need remote channels to communicate different processes. Standard Channels would not work. For instance, the following code would block at the `take!`. Worker 4 will receive a different copy of the channel and will put values in it. The channel defined in the main process will remain empty and this will make the take! to block. \n",
- "\n",
"```julia\n",
"chnl = Channel{Int}()\n",
"@spawnat 4 begin\n",
@@ -632,7 +655,10 @@
" close(chnl)\n",
"end\n",
"take!(chnl)\n",
- "```"
+ "```\n",
+ "\n",
+ "You really need remote channels to communicate different processes. Standard Channels would not work. For instance, the following code would block at the `take!`. Worker 4 will receive a different copy of the channel and will put values in it. The channel defined in the main process will remain empty and this will make the take! to block. \n",
+ "\n"
]
},
{
@@ -818,6 +844,16 @@
"q_1_check(answer)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9c4d4900",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "why_q1()"
+ ]
+ },
{
"cell_type": "markdown",
"id": "dbe373d1",
@@ -852,6 +888,16 @@
"q_2_check(answer)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e7c25fc4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "why_q2()"
+ ]
+ },
{
"cell_type": "markdown",
"id": "c561a73d",
@@ -877,6 +923,16 @@
"x"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7b25a83f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "why_q3()"
+ ]
+ },
{
"cell_type": "markdown",
"id": "835080aa",
@@ -903,6 +959,16 @@
"x"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "96b84cb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "why_q4()"
+ ]
+ },
{
"cell_type": "markdown",
"id": "9e985c61",
@@ -1317,6 +1383,41 @@
"We have seen the basics of distributed computing in Julia. The programming model is essentially an extension of tasks and channels to parallel computations on multiple machines. The low-level functions are `remotecall` and `RemoteChannel`, but there are other functions and macros like `pmap` and `@distributed` that simplify the implementation of parallel algorithms."
]
},
+ {
+ "cell_type": "markdown",
+ "id": "a75aa3bb",
+ "metadata": {},
+ "source": [
+ "## Exercises"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3153bd90",
+ "metadata": {},
+ "source": [
+ "### Exercise 1\n",
+ "\n",
+ "Implement this \"simple\" algorithm (the telephone game):\n",
+ "\n",
+ "Worker 1 generates a message (an integer). Worker 1 sends the message to worker 2. Worker 2 receives the message, increments the message by 1, and sends the result to worker 3. Worker 3 receives the message, increments the message by 1, and sends the result to worker 4. Etc. The last worker sends back the message to worker 1 closing the ring. See the next figure."
+ ]
+ },
+ {
+ "attachments": {
+ "g5148-2.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "6a485db2",
+ "metadata": {},
+ "source": [
+ "
In this notebook, we will learn the basics of distributed computing in Julia. In particular, we will focus on the Distributed module available in the Julia standard library. The main topics we are going to cover are:
In this notebook, we will learn the basics of distributed computing in Julia. In particular, we will focus on the Distributed module available in the Julia standard library. The main topics we are going to cover are:
How to create Julia processes
How to execute code remotely
@@ -7548,6 +7582,31 @@ a.anchor-link {
endq_1_check(answer)=answer_checker(answer,"a")q_2_check(answer)=answer_checker(answer,"b")
+functionwhy_q1()
+msg="""
+ We send the matrix (16 entries) and then we receive back the result (1 extra integer). Thus, the total number of transferred integers in 17.
+ """
+display(msg)
+end
+functionwhy_q2()
+msg="""
+ Even though we only use a single entry of the matrix in the remote worker, the entire matrix is captured and sent to the worker. Thus, we will transfer 17 integers like in Question 1.
+ """
+display(msg)
+end
+functionwhy_q3()
+msg="""
+ The value of x will still be zero since the worker receives a copy of the matrix and it modifies this copy, not the original one.
+ """
+display(msg)
+end
+functionwhy_q4()
+msg="""
+ In this case, the code a[2]=2 is executed in the main process. Since the matrix is already in the main process, it is not needed to create and send a copy of it. Thus, the code modifies the original matrix and the value of x will be 2.
+ """
+display(msg)
+end
+println("🥳 Well done! ")
@@ -8233,8 +8292,7 @@ bottlenecks. Being aware of the data we are moving when using functions such as
You really need remote channels to communicate different processes. Standard Channels would not work. For instance, the following code would block at the take!. Worker 4 will receive a different copy of the channel and will put values in it. The channel defined in the main process will remain empty and this will make the take! to block.
chnl=Channel{Int}()@spawnat4beginforiin1:5put!(chnl,i)
@@ -8243,6 +8301,7 @@ bottlenecks. Being aware of the data we are moving when using functions such as
endtake!(chnl)
+
You really need remote channels to communicate different processes. Standard Channels would not work. For instance, the following code would block at the take!. Worker 4 will receive a different copy of the channel and will put values in it. The channel defined in the main process will remain empty and this will make the take! to block.
Implement this "simple" algorithm (the telephone game):
+
Worker 1 generates a message (an integer). Worker 1 sends the message to worker 2. Worker 2 receives the message, increments the message by 1, and sends the result to worker 3. Worker 3 receives the message, increments the message by 1, and sends the result to worker 4. Etc. The last worker sends back the message to worker 1 closing the ring. See the next figure.
This document was generated with Documenter.jl version 1.1.1 on Monday 16 October 2023. Using Julia version 1.9.3.
+
Settings
This document was generated with Documenter.jl version 1.5.0 on Monday 19 August 2024. Using Julia version 1.10.4.
diff --git a/dev/julia_jacobi_src/index.html b/dev/julia_jacobi_src/index.html
index cf072a6..60b2660 100644
--- a/dev/julia_jacobi_src/index.html
+++ b/dev/julia_jacobi_src/index.html
@@ -7333,11 +7333,12 @@ a.anchor-link {
if (!diagrams.length) {
return;
}
- const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.5.0/mermaid.esm.min.mjs")).default;
+ const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.7.0/mermaid.esm.min.mjs")).default;
const parser = new DOMParser();
mermaid.initialize({
maxTextSize: 100000,
+ maxEdges: 100000,
startOnLoad: false,
fontFamily: window
.getComputedStyle(document.body)
@@ -7408,7 +7409,8 @@ a.anchor-link {
let results = null;
let output = null;
try {
- const { svg } = await mermaid.render(id, raw, el);
+ let { svg } = await mermaid.render(id, raw, el);
+ svg = cleanMermaidSvg(svg);
results = makeMermaidImage(svg);
output = document.createElement("figure");
results.map(output.appendChild, output);
@@ -7423,6 +7425,38 @@ a.anchor-link {
parent.appendChild(output);
}
+
+ /**
+ * Post-process to ensure mermaid diagrams contain only valid SVG and XHTML.
+ */
+ function cleanMermaidSvg(svg) {
+ return svg.replace(RE_VOID_ELEMENT, replaceVoidElement);
+ }
+
+
+ /**
+ * A regular expression for all void elements, which may include attributes and
+ * a slash.
+ *
+ * @see https://developer.mozilla.org/en-US/docs/Glossary/Void_element
+ *
+ * Of these, only ` ` is generated by Mermaid in place of `\n`,
+ * but _any_ "malformed" tag will break the SVG rendering entirely.
+ */
+ const RE_VOID_ELEMENT =
+ /<\s*(area|base|br|col|embed|hr|img|input|link|meta|param|source|track|wbr)\s*([^>]*?)\s*>/gi;
+
+ /**
+ * Ensure a void element is closed with a slash, preserving any attributes.
+ */
+ function replaceVoidElement(match, tag, rest) {
+ rest = rest.trim();
+ if (!rest.endsWith('/')) {
+ rest = `${rest} /`;
+ }
+ return `<${tag} ${rest}>`;
+ }
+
void Promise.all([...diagrams].map(renderOneMarmaid));
});
diff --git a/dev/julia_mpi.ipynb b/dev/julia_mpi.ipynb
new file mode 100644
index 0000000..9fdd89f
--- /dev/null
+++ b/dev/julia_mpi.ipynb
@@ -0,0 +1,1848 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "7606d30a",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Programming large-scale parallel systems"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4ac1e5d9",
+ "metadata": {},
+ "source": [
+ "# Distributed computing with MPI"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a341be2e",
+ "metadata": {},
+ "source": [
+ "## Contents\n",
+ "\n",
+ "\n",
+ "In this notebook, we will learn the basics of parallel computing using the Message Passing Interface (MPI) from Julia. In particular, we will learn:\n",
+ "\n",
+ "- How to run parallel MPI code in Julia\n",
+ "- How to use basic collective communication directives\n",
+ "- How to use basic point-to-point communication directives\n",
+ "\n",
+ "For further information on how to use MPI from Julia see https://github.com/JuliaParallel/MPI.jl\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8862079b",
+ "metadata": {},
+ "source": [
+ "## What is MPI ?\n",
+ "\n",
+ "- MPI stands for the \"Message Passing Interface\"\n",
+ "- It is a standardized library specification for communication between parallel processes in distributed-memory systems.\n",
+ "- It is the gold-standard for distributed computing in HPC systems since the 90s\n",
+ "- It is huge: the MPI standard has more than 1k pages (see https://www.mpi-forum.org/docs/mpi-4.0/mpi40-report.pdf)\n",
+ "- There are several implementations of this standard (OpenMPI, MPICH, IntelMPI)\n",
+ "- The interface is in C and FORTRAN (C++ was deprecated)\n",
+ "- There are Julia bindings via the package MPI.jl https://github.com/JuliaParallel/MPI.jl"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "99c6febb",
+ "metadata": {},
+ "source": [
+ "### What is MPI.jl ?\n",
+ "\n",
+ "- It is not a Julia implementation of the MPI standard\n",
+ "- It is a wrapper to the C interface of MPI\n",
+ "- You need a C MPI installation in your system\n",
+ "\n",
+ "\n",
+ "MPI.jl provides a convenient Julia API to access MPI. For instance, this is how you get the id (rank) of the current process.\n",
+ "\n",
+ "```julia\n",
+ "comm = MPI.COMM_WORLD\n",
+ "rank = MPI.Comm_rank(comm)\n",
+ "```\n",
+ "\n",
+ "Internally, MPI.jl uses `ccall` which is a mechanism that allows you to call C functions from Julia. In this, example we are calling the C function `MPI_Comm_rank` from the underlying MPI installation.\n",
+ "\n",
+ "```julia\n",
+ "comm = MPI.COMM_WORLD \n",
+ "rank_ref = Ref{Cint}()\n",
+ "ccall((:MPI_Comm_rank, MPI.API.libmpi), Cint, (MPI.API.MPI_Comm, Ptr{Cint}), comm, rank_ref)\n",
+ "rank = Int(rank_ref[])\n",
+ "```\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "82e6e98f",
+ "metadata": {},
+ "source": [
+ "### Installing MPI in Julia\n",
+ "\n",
+ "The Jupyter Julia kernel installed by IJulia activates the folder where the notebook is located as the default environment, which causes the main process and the worker processes to not share the same environment. Therefore, we need to set the environment as the global environment."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "705c2439-c805-4818-be73-342182f7b7a0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "] activate"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e99c7676-989e-4e91-b65e-ebca2d5626a4",
+ "metadata": {},
+ "source": [
+ "MPI can be installed as any other Julia package using the package manager."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0b44409e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "] add MPI"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "abc6f017",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Note: The package you have installed it is the Julia interface to MPI, called MPI.jl. Note that it is not a MPI library by itself. It is just a thin wrapper between MPI and Julia. To use this interface, you need an actual MPI library installed in your system such as OpenMPI or MPICH. Julia downloads and installs a MPI library for you, but it is also possible to use a MPI library already available in your system. This is useful, e.g., when running on HPC clusters. See the documentation of MPI.jl for further details. See more information in https://github.com/JuliaParallel/MPI.jl\n",
+ "
\n",
+ "Note: Note that the Julia syntax is almost 1-to-1 to the C one. The key difference is that in Julia MPI routines are written as `MPI.X` where in C are written `MPI_X`.\n",
+ "
\n",
+ "\n",
+ "\n",
+ "* It is mandatory to initialize MPI before using MPI procedures.\n",
+ "* In C, all processes must call `MPI_Finalize` before exiting.\n",
+ "* In Julia, either all or none process must call `MPI.Finalize()`.\n",
+ "* Once finalized, MPI cannot be re"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "87dfb768",
+ "metadata": {},
+ "source": [
+ "### An incorrect MPI program\n",
+ "\n",
+ "```julia\n",
+ "using MPI\n",
+ "MPI.Init()\n",
+ "@assert rand(1:10) != 2\n",
+ "MPI.Finalize()\n",
+ "```\n",
+ "\n",
+ "In some process `rand(1:10)` might be 2 and the program will stop without reaching `MPI.Finalize()`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "65de4419",
+ "metadata": {},
+ "source": [
+ "### Solving the issue"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4a8ebeff",
+ "metadata": {},
+ "source": [
+ "Premature finalization of a program is done with `MPI.Abort`.\n",
+ "\n",
+ "```julia\n",
+ "using MPI\n",
+ "MPI.Init()\n",
+ "if rand(1:10) != 2\n",
+ " errorcode = -1\n",
+ " MPI.Abort(MPI.COMM_WORLD,errorcode)\n",
+ "end\n",
+ "MPI.Finalize()\n",
+ "```\n",
+ "\n",
+ "* There is no need to call `MPI.Abort` in all ranks."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fa7145bc",
+ "metadata": {},
+ "source": [
+ "### Read the docs\n",
+ "\n",
+ "Not sure if an MPI routine needs to be called by all the ranks? Read the documentation, and/or the MPI standard."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a74c7c72",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "? MPI.Finalize"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a534e3a2",
+ "metadata": {},
+ "source": [
+ "## Basic information about MPI processes\n",
+ "\n",
+ "The following cells give information about MPI processes, such as the rank id, the total number of processes and the name of the host running the code respectively. Before calling this functions one needs to initialize MPI."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "96f7c14e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "using MPI\n",
+ "MPI.Init()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fe202985",
+ "metadata": {},
+ "source": [
+ " Current rank (process) id"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bd8232f5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "comm = MPI.COMM_WORLD\n",
+ "rank = MPI.Comm_rank(comm)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dd40d1dc",
+ "metadata": {},
+ "source": [
+ "Number of available processes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0befa408",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nranks = MPI.Comm_size(comm)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d1eeaf81",
+ "metadata": {},
+ "source": [
+ "Name of the current host"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ff01adcf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "MPI.Get_processor_name()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f1a502a3",
+ "metadata": {},
+ "source": [
+ "Note that this note notebook is not running with different MPI processes (yet). So using MPI will only make sense later when we add more processes."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "133327e2",
+ "metadata": {},
+ "source": [
+ "### Hello-world example\n",
+ "\n",
+ "Using these functions we can create the a classic MPI hello world example. This example (or variations thereof) is used in practice to check how MPI ranks are mapped to available processing units in a given computing system.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a154b55e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "using MPI\n",
+ "MPI.Init()\n",
+ "comm = MPI.COMM_WORLD\n",
+ "nranks = MPI.Comm_size(comm)\n",
+ "rank = MPI.Comm_rank(comm)\n",
+ "host = MPI.Get_processor_name()\n",
+ "println(\"Hello from $host, I am process $rank of $nranks processes!\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "baddbba1",
+ "metadata": {},
+ "source": [
+ "### Hello world in C\n",
+ "\n",
+ "```C\n",
+ "#include \n",
+ "#include \n",
+ "int main(int argc, char** argv) {\n",
+ " MPI_Init(NULL, NULL);\n",
+ " int world_size;\n",
+ " MPI_Comm_size(MPI_COMM_WORLD, &world_size);\n",
+ " int world_rank;\n",
+ " MPI_Comm_rank(MPI_COMM_WORLD, &world_rank);\n",
+ " char processor_name[MPI_MAX_PROCESSOR_NAME];\n",
+ " int name_len;\n",
+ " MPI_Get_processor_name(processor_name, &name_len);\n",
+ " printf(\"Hello from %s, I am rank %d of %d ranks!\\n\",\n",
+ " processor_name, world_rank, world_size);\n",
+ " MPI_Finalize();\n",
+ "}\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e3901c57",
+ "metadata": {},
+ "source": [
+ "## Running MPI code"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8376135d",
+ "metadata": {},
+ "source": [
+ "### Creating MPI processes (aka ranks)\n",
+ "\n",
+ "- MPI processes are created with the driver program `mpiexec`\n",
+ "- `mpiexec` takes an application and runs it on different ranks\n",
+ "- The application calls MPI directives to communicate between these ranks\n",
+ "- The application can be Julia running your script in particular.\n"
+ ]
+ },
+ {
+ "attachments": {
+ "fig23.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "a458c714",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "044aaeec",
+ "metadata": {},
+ "source": [
+ "### Execution model\n",
+ "\n",
+ "- MPI programs are typically run with a Single Program Multiple Data (SPMD) model\n",
+ "- But the standard supports Multiple Program Multiple Data (MPMD)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5f76fb65",
+ "metadata": {},
+ "source": [
+ "### Hello world\n",
+ "\n",
+ "Julia code typically needs to be in a file to run it in with MPI. Let's us write our hello world in a file:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e03f35c5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "code = raw\"\"\"\n",
+ "using MPI\n",
+ "MPI.Init()\n",
+ "comm = MPI.COMM_WORLD\n",
+ "nranks = MPI.Comm_size(comm)\n",
+ "rank = MPI.Comm_rank(comm)\n",
+ "println(\"Hello, I am process $rank of $nranks processes!\")\n",
+ "MPI.Finalize()\n",
+ "\"\"\"\n",
+ "filename = tempname()*\".jl\"\n",
+ "write(filename,code);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f13946dd",
+ "metadata": {},
+ "source": [
+ "Now, we can run it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dbe654dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "using MPI\n",
+ "run(`$(mpiexec()) -np 4 julia --project=. $filename`);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "651f26ae",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Note: Function `mpiexec` provided by `MPI.jl` is a convenient way of accessing the `mpiexec` program that matches the MPI installation used my Julia.\n",
+ "
\n",
+ "Note: Note that the rank ids start with 0.\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a958c015",
+ "metadata": {},
+ "source": [
+ "### Another way to launch MPI code\n",
+ "\n",
+ "In the Hello world example above we have created an auxiliary file to run the code with MPI. This can be annoying specially if you are working in a jupyter notebook. With this other syntax you can skip creating the auxiliary file. we use `quote` which is part of the [meta-programming capabilities of Julia](https://docs.julialang.org/en/v1/manual/metaprogramming/). "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "359e569b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "code = quote\n",
+ " using MPI\n",
+ " MPI.Init()\n",
+ " comm = MPI.COMM_WORLD\n",
+ " nranks = MPI.Comm_size(comm)\n",
+ " rank = MPI.Comm_rank(comm)\n",
+ " println(\"Hello, I am process $rank of $nranks processes!\")\n",
+ " MPI.Finalize()\n",
+ "end\n",
+ "run(`$(mpiexec()) -np 4 julia --project=. -e $code`);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5ec1c52a",
+ "metadata": {},
+ "source": [
+ "### Data availability"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c8a5e099",
+ "metadata": {},
+ "source": [
+ "Note that mpiexec creates new processes which are different from the process running this notebook. In particular, these new processes will not see any variables or function definitions in the current notebook. So, the full MPI program needs to be in the source file passed to Julia or the quote block."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "16bb608a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "foo() = print(\"Hi there!\")\n",
+ "code = quote\n",
+ " using MPI\n",
+ " MPI.Init()\n",
+ " foo()\n",
+ "end\n",
+ "run(`$(mpiexec()) -np 3 julia --project=. -e $code`);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4b455f98",
+ "metadata": {},
+ "source": [
+ "So, the full MPI program needs to be in the source file passed to Julia or the quote block. In practice, long MPI programms are written as Julia packages using several files, which are then loaded by each MPI process. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b816659a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "code = quote\n",
+ " foo() = print(\"Hi there!\")\n",
+ " using MPI\n",
+ " MPI.Init()\n",
+ " foo()\n",
+ "end\n",
+ "run(`$(mpiexec()) -np 3 julia --project=. -e $code`);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a37daef2",
+ "metadata": {},
+ "source": [
+ "## Point-to-point communication\n",
+ "\n",
+ "MPI provides point-to-point communication directives for arbitrary communication between processes. Point-to-point communications are two-sided: there is a sender and a receiver. Here, we will discuss these basic directives:\n",
+ "\n",
+ "- `MPI_Send`, and `MPI_Recv!` (*blocking directives*)\n",
+ "- `MPI_Isend`, and `MPI_Irecv!` (*non-blocking directives, aka incomplete directives*)\n",
+ "- `MPI_Bsend`, `MPI_Ssend`, and `MPI_Rsend` (*advanced communication modes*)"
+ ]
+ },
+ {
+ "attachments": {
+ "fig_p2p.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "14674ca9",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ead20fc0",
+ "metadata": {},
+ "source": [
+ "## Blocking send and receive\n",
+ "\n",
+ "In Julia:\n",
+ "\n",
+ "```julia\n",
+ "MPI.Send(sndbuf, comm; dest, tag)\n",
+ "_, status = MPI.Recv!(rcvbuf, comm, MPI.Status; source, tag)\n",
+ "```\n",
+ "In C:\n",
+ "```C\n",
+ "int MPI_Send(const void *sndbuf, int count, MPI_Datatype datatype, int dest,\n",
+ " int tag, MPI_Comm comm)\n",
+ "int MPI_Recv(void *rcvbuf, int count, MPI_Datatype datatype,\n",
+ " int source, int tag, MPI_Comm comm, MPI_Status *status)\n",
+ "```\n",
+ "\n",
+ "Key arguments:\n",
+ "\n",
+ "* `sndbuf` data to send.\n",
+ "* `rcvbuf` space to store the received data.\n",
+ "* `source` rank of the sender.\n",
+ "* `dest` rank of the receiver.\n",
+ "* `tag`. Might be used to distinguish between different kinds of messages (i.e., the \"subject\" in an email)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "be4fae1c",
+ "metadata": {},
+ "source": [
+ "### Example"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c2301992",
+ "metadata": {},
+ "source": [
+ "Send 5 integers from rank 2 to rank 3."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "61fe5db3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "code = quote\n",
+ " using MPI\n",
+ " MPI.Init()\n",
+ " comm = MPI.COMM_WORLD\n",
+ " rank = MPI.Comm_rank(comm)\n",
+ " if rank == 2\n",
+ " sndbuf = [1,2,3,5,8]\n",
+ " MPI.Send(sndbuf, comm; dest=3, tag=0)\n",
+ " end\n",
+ " if rank == 3\n",
+ " rcvbuf = zeros(Int,5)\n",
+ " MPI.Recv!(rcvbuf, comm, MPI.Status; source=2, tag=0)\n",
+ " @show rcvbuf\n",
+ " end\n",
+ "end\n",
+ "run(`$(mpiexec()) -np 4 julia --project=. -e $code`);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ee680aa5",
+ "metadata": {},
+ "source": [
+ "### Any source, any tag\n",
+ "\n",
+ "We can use\n",
+ "\n",
+ "* `source = MPI.ANY_SOURCE`\n",
+ "* `tag = MPI.ANY_TAG`\n",
+ "\n",
+ "If we want to receive messages from any source and/or with any tag.\n",
+ "\n",
+ "\n",
+ "
\n",
+ "Note: These can only be used by the receiver, not the sender. Moreover there is no option to send to any destination.\n",
+ "
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1f0cb6c8",
+ "metadata": {},
+ "source": [
+ "### Example"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2add5065",
+ "metadata": {},
+ "source": [
+ "Send 5 integers from rank 2 to rank 3."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ccdf660a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "code = quote\n",
+ " using MPI\n",
+ " MPI.Init()\n",
+ " comm = MPI.COMM_WORLD\n",
+ " rank = MPI.Comm_rank(comm)\n",
+ " if rank == 2\n",
+ " sndbuf = [1,2,3,5,8]\n",
+ " MPI.Send(sndbuf, comm; dest=3, tag=0)\n",
+ " end\n",
+ " if rank == 3\n",
+ " rcvbuf = zeros(Int,5)\n",
+ " source = MPI.ANY_SOURCE\n",
+ " tag = MPI.ANY_TAG\n",
+ " MPI.Recv!(rcvbuf, comm, MPI.Status; source, tag)\n",
+ " @show rcvbuf\n",
+ " end\n",
+ "end\n",
+ "run(`$(mpiexec()) -np 4 julia --project=. -e $code`);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7833da72",
+ "metadata": {},
+ "source": [
+ "### Who was the sender? Which was the tag?\n",
+ "\n",
+ "When using `MPI.ANY_SOURCE` and `MPI.ANY_TAG` it might be still useful to know which was the sender and which tag was used. This information is given by a `MPI.Status` object.\n",
+ "\n",
+ "\n",
+ "```julia\n",
+ "_, status = MPI.Recv!(rcvbuf, comm, MPI.Status; source, tag)\n",
+ "status.source # Gives the source\n",
+ "status.tag # Gives the tag\n",
+ "```\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2f037032",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "code = quote\n",
+ " using MPI\n",
+ " MPI.Init()\n",
+ " comm = MPI.COMM_WORLD\n",
+ " rank = MPI.Comm_rank(comm)\n",
+ " if rank == 2\n",
+ " sndbuf = [1,2,3,5,8]\n",
+ " MPI.Send(sndbuf, comm; dest=3, tag=0)\n",
+ " end\n",
+ " if rank == 3\n",
+ " rcvbuf = zeros(Int,5)\n",
+ " source = MPI.ANY_SOURCE\n",
+ " tag = MPI.ANY_TAG\n",
+ " _, status = MPI.Recv!(rcvbuf, comm, MPI.Status; source, tag)\n",
+ " @show rcvbuf\n",
+ " @show status.source\n",
+ " @show status.tag\n",
+ " end\n",
+ "end\n",
+ "run(`$(mpiexec()) -np 4 julia --project=. -e $code`);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "590bc407",
+ "metadata": {},
+ "source": [
+ "### Which is the incoming message size?\n",
+ "\n",
+ "Note that we need to provide a receive buffer with the right size, but it general we might do not know which is the size of the incoming message. This can be solved using an `MPI_Probe`. It works similar to `MPI_Recv`, but instead of receiving the message only receives information about the message (source, tag, and also message size).\n",
+ "\n",
+ "```julia\n",
+ "status = MPI.Probe(comm,MPI.Status; source, tag)\n",
+ "count = MPI.Get_count(status, T)\n",
+ "```\n",
+ "```C\n",
+ "int MPI_Probe(int source, int tag, MPI_Comm comm, MPI_Status *status)\n",
+ "int MPI_Get_count(const MPI_Status *status, MPI_Datatype datatype, int *count)\n",
+ "```\n",
+ "\n",
+ "We can get the message size from the status object using function `MPI_Get_count`. We can also get the source and tag from the status object as shown before."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "517d052e",
+ "metadata": {},
+ "source": [
+ "### Example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b54af36",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "code = quote\n",
+ " using MPI\n",
+ " MPI.Init()\n",
+ " comm = MPI.COMM_WORLD\n",
+ " rank = MPI.Comm_rank(comm)\n",
+ " if rank == 2\n",
+ " sndbuf = [1,2,3,5,8]\n",
+ " MPI.Send(sndbuf, comm; dest=3, tag=0)\n",
+ " end\n",
+ " if rank == 3\n",
+ " source = MPI.ANY_SOURCE\n",
+ " tag = MPI.ANY_TAG\n",
+ " status = MPI.Probe(comm,MPI.Status; source, tag)\n",
+ " count = MPI.Get_count(status,Int)\n",
+ " println(\"I am about to receive $count integers.\")\n",
+ " rcvbuf = zeros(Int,count) \n",
+ " MPI.Recv!(rcvbuf, comm, MPI.Status; source, tag)\n",
+ " @show rcvbuf\n",
+ " end\n",
+ "end\n",
+ "run(`$(mpiexec()) -np 4 julia --project=. -e $code`);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "04bdb01f",
+ "metadata": {},
+ "source": [
+ "### Blocking semantics\n",
+ "\n",
+ "Functions `MPI_Send` and `MPI_Recv` are *blocking*.\n",
+ "\n",
+ "This means:\n",
+ "\n",
+ "- It is safe to re-write the send buffer once `MPI_Send` returns.\n",
+ "- The received message is guaranteed to be fully available in the receive buffer once `MPI_Recv` returns."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2299bf78",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "code = quote\n",
+ " using MPI\n",
+ " MPI.Init()\n",
+ " comm = MPI.COMM_WORLD\n",
+ " rank = MPI.Comm_rank(comm)\n",
+ " if rank == 2\n",
+ " sndbuf = [1,2,3,5,8]\n",
+ " MPI.Send(sndbuf, comm; dest=3, tag=0)\n",
+ " sndbuf .= 0 # This is fine. Send has returned.\n",
+ " end\n",
+ " if rank == 3\n",
+ " rcvbuf = zeros(Int,5)\n",
+ " MPI.Recv!(rcvbuf, comm, MPI.Status; source=2, tag=0)\n",
+ " # recvbuf will have the incomming message fore sure. Recv! has returned.\n",
+ " @show rcvbuf\n",
+ " end\n",
+ "end\n",
+ "run(`$(mpiexec()) -np 4 julia --project=. -e $code`);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "245893cf",
+ "metadata": {},
+ "source": [
+ "However:\n",
+ "- We cannot assume synchronization between sender and receiver. I.e., *blocking* is not the same as *synchronous*. We cannot assume that a receive has been posted once `MPI_Send` returns as the underlying implementation might copy the send message into an internal buffer and return before any matching `MPI_Recv` started.\n",
+ "\n",
+ "- A blocking send is not *synchronous*, but we cannot assume that it is *asynchronous*. The underlying implementation might not use any auxiliary buffer and wait for a matching receive. Assuming buffering is erroneous and can lead to dead locks."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "59fcac63",
+ "metadata": {},
+ "source": [
+ "### Incorrect program\n",
+ "\n",
+ "The following program will or will not work depending whether the underlying implementation uses buffering. On my laptop, it works with `n=1`, but leads to a dead lock when `n=10000`. The MPI implementation decided to buffer or not depending on the message size."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "42fb8089",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "code = quote\n",
+ " using MPI\n",
+ " MPI.Init()\n",
+ " comm = MPI.COMM_WORLD\n",
+ " rank = MPI.Comm_rank(comm)\n",
+ " n = 1\n",
+ " sndbuf = fill(rank,n)\n",
+ " rcvbuf = zeros(Int,n)\n",
+ " if rank == 2\n",
+ " MPI.Send(sndbuf, comm; dest=3, tag=0)\n",
+ " MPI.Recv!(rcvbuf, comm, MPI.Status; source=3, tag=0)\n",
+ " end\n",
+ " if rank == 3\n",
+ " MPI.Send(sndbuf, comm; dest=2, tag=0)\n",
+ " MPI.Recv!(rcvbuf, comm, MPI.Status; source=2, tag=0)\n",
+ " end\n",
+ " @show (rcvbuf[1],rank)\n",
+ "end\n",
+ "run(`$(mpiexec()) -np 4 julia --project=. -e $code`);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8b7eeec2",
+ "metadata": {},
+ "source": [
+ "### Correct program\n",
+ "\n",
+ "We can fix the program by smartly ordering the sends and the receives. This should work for any value of `n` as long as we have enough memory in the system."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "94c8d5e9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "code = quote\n",
+ " using MPI\n",
+ " MPI.Init()\n",
+ " comm = MPI.COMM_WORLD\n",
+ " rank = MPI.Comm_rank(comm)\n",
+ " n = 10000\n",
+ " sndbuf = fill(rank,n)\n",
+ " rcvbuf = zeros(Int,n)\n",
+ " if rank == 2\n",
+ " MPI.Send(sndbuf, comm; dest=3, tag=0)\n",
+ " MPI.Recv!(rcvbuf, comm, MPI.Status; source=3, tag=0)\n",
+ " end\n",
+ " if rank == 3\n",
+ " MPI.Recv!(rcvbuf, comm, MPI.Status; source=2, tag=0)\n",
+ " MPI.Send(sndbuf, comm; dest=2, tag=0)\n",
+ " end\n",
+ " @show (rcvbuf[1],rank)\n",
+ "end\n",
+ "run(`$(mpiexec()) -np 4 julia --project=. -e $code`);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2b37ed85",
+ "metadata": {},
+ "source": [
+ "Another solution is to use `MPI_Sendrecv`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9c0c84f0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "code = quote\n",
+ " using MPI\n",
+ " MPI.Init()\n",
+ " comm = MPI.COMM_WORLD\n",
+ " rank = MPI.Comm_rank(comm)\n",
+ " n = 10000\n",
+ " sndbuf = fill(rank,n)\n",
+ " rcvbuf = zeros(Int,n)\n",
+ " if rank == 2\n",
+ " MPI.Sendrecv!(sndbuf,rcvbuf, comm;dest=3,source=3,sendtag=0,recvtag=0)\n",
+ " end\n",
+ " if rank == 3\n",
+ " MPI.Sendrecv!(sndbuf,rcvbuf, comm;dest=2,source=2,sendtag=0,recvtag=0)\n",
+ " end\n",
+ " @show (rcvbuf[1],rank)\n",
+ "end\n",
+ "run(`$(mpiexec()) -np 4 julia --project=. -e $code`);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4fca501c",
+ "metadata": {},
+ "source": [
+ "## Communication modes\n",
+ "\n",
+ "In all cases, it is safe to reuse the send buffer once the corresponding send returns. I.e., all the following sends are *complete* MPI operations. However, there are some important differences.\n",
+ "\n",
+ "### Standard\n",
+ "\n",
+ "* `MPI_Send` \n",
+ "* Programmer cannot make any assumptions whether the message is buffered or synchronous.\n",
+ "* This is up to the system.\n",
+ "\n",
+ "### Buffered\n",
+ "\n",
+ "* `MPI_Bsend`\n",
+ "* Programmer provides additional internal buffer space with function `MPI_Buffer_attach`.\n",
+ "* `MPI_Bsend` completes when message is copied into a local buffer.\n",
+ "* Erroneous if buffer space is insufficient.\n",
+ "\n",
+ "### Synchronous\n",
+ "\n",
+ "* `MPI_Ssend`\n",
+ "* The send will only return if a matching receive was posted.\n",
+ "* No buffering, no extra copy, but easy to get deadlocks\n",
+ "* It can be started whether or not a matching receive was posted.\n",
+ "\n",
+ "### Ready\n",
+ "\n",
+ "* `MPI_Rsend`\n",
+ "* It may be started only if the matching receive is already posted.\n",
+ "* Erroneous if there is no matching receive yet.\n",
+ "* Otherwise, same as an `MPI_Ssend`.\n",
+ "\n",
+ "\n",
+ " All these send types are matched with a `MPI_Recv`. I.e., there is no `MPI_Brecv`, `MPI_Srecv`, `MPI_Rrecv`. For further information about the communication modes, refer to [this section](https://www.mpi-forum.org/docs/mpi-2.2/mpi22-report/node53.htm) of the MPI standard.\n",
+ " \n",
+ " \n",
+ "
\n",
+ "Note: `MPI_Bsend`, `MPI_Ssend`, and `MPI_Rsend` are not exposed in the Julia bindings via a high-level interface like for `MPI.Send`, but they can be accessed using the low-level bindings in the submodule `MPI.API` (not shown in this notebook).\n",
+ "
\n",
+ " \n",
+ " \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7a935b14",
+ "metadata": {},
+ "source": [
+ "## Non-blocking send and receive\n",
+ "\n",
+ "- They return immediately.\n",
+ "- The send and receive is not completed when the function returns. I.e. they are *incomplete* operations.\n",
+ "- `MPI_Wait` used to wait for completion of the send and/or receive.\n",
+ "\n",
+ "- Used to overlap communication and computation (\"latency-hiding\").\n",
+ "\n",
+ "\n",
+ "```julia\n",
+ "request = MPI.Isend(sndbuf, comm; dest, tag)\n",
+ "request = MPI.Irecv!(rcvbuf, comm; source, tag)\n",
+ "```\n",
+ "\n",
+ "```C\n",
+ "int MPI_Isend(const void *sndbuf, int count, MPI_Datatype datatype, int dest,\n",
+ " int tag, MPI_Comm comm, MPI_Request *request)\n",
+ "int MPI_Irecv(void *rcvbuf, int count, MPI_Datatype datatype,\n",
+ " int source, int tag, MPI_Comm comm, MPI_Request *request)\n",
+ "\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "85903e61",
+ "metadata": {},
+ "source": [
+ "### Example\n",
+ "\n",
+ "Send 5 integers from rank 2 to rank 3. Both ranks do some work while messages are being communicated."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "42284c69",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "code = quote\n",
+ " using MPI\n",
+ " MPI.Init()\n",
+ " work() = sum(rand(1000))\n",
+ " comm = MPI.COMM_WORLD\n",
+ " rank = MPI.Comm_rank(comm)\n",
+ " if rank == 2\n",
+ " sndbuf = [1,2,3,5,8]\n",
+ " request = MPI.Isend(sndbuf, comm; dest=3, tag=0)\n",
+ " work()\n",
+ " MPI.Wait(request)\n",
+ " end\n",
+ " if rank == 3\n",
+ " rcvbuf = zeros(Int,5)\n",
+ " request = MPI.Irecv!(rcvbuf, comm; source=2, tag=0)\n",
+ " work()\n",
+ " MPI.Wait(request)\n",
+ " @show rcvbuf\n",
+ " end\n",
+ "end\n",
+ "run(`$(mpiexec()) -np 4 julia --project=. -e $code`);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0180c576",
+ "metadata": {},
+ "source": [
+ "### Incorrect program\n",
+ "\n",
+ "This program in incorrect both on the send and the receive side.\n",
+ "\n",
+ "- One needs to wait for completion before reseting the send buffer\n",
+ "- One needs to wait for completion before using the receive buffer\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "52991807",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "code = quote\n",
+ " using MPI\n",
+ " MPI.Init()\n",
+ " comm = MPI.COMM_WORLD\n",
+ " rank = MPI.Comm_rank(comm)\n",
+ " if rank == 2\n",
+ " sndbuf = [1,2,3,5,8]\n",
+ " request = MPI.Isend(sndbuf, comm; dest=3, tag=0)\n",
+ " sndbuf .= 10 # We cannot set the sndbuf before MPI.Wait.\n",
+ " MPI.Wait(request)\n",
+ " end\n",
+ " if rank == 3\n",
+ " rcvbuf = zeros(Int,5)\n",
+ " request = MPI.Irecv!(rcvbuf, comm; source=2, tag=0)\n",
+ " @show rcvbuf # Not guaranteed to have the correct value.\n",
+ " MPI.Wait(request)\n",
+ " end\n",
+ "end\n",
+ "run(`$(mpiexec()) -np 4 julia --project=. -e $code`);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4cc264f1",
+ "metadata": {},
+ "source": [
+ "### Which is the incoming message size?\n",
+ "\n",
+ "If we use `MPI_Probe` we miss the opportunity to do local work before a send is started."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "03b11966",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "code = quote\n",
+ " using MPI\n",
+ " MPI.Init()\n",
+ " work() = sum(rand(1000))\n",
+ " comm = MPI.COMM_WORLD\n",
+ " rank = MPI.Comm_rank(comm)\n",
+ " if rank == 2\n",
+ " sleep(5) # Sleep 5 seconds\n",
+ " sndbuf = [1,2,3,5,8]\n",
+ " request = MPI.Isend(sndbuf, comm; dest=3, tag=0)\n",
+ " MPI.Wait(request)\n",
+ " end\n",
+ " if rank == 3\n",
+ " # We are going to wait here for about 5 seconds\n",
+ " # Missing the opportunity to do some useful work\n",
+ " status = MPI.Probe(comm,MPI.Status; source=2, tag=0)\n",
+ " count = MPI.Get_count(status,Int)\n",
+ " rcvbuf = zeros(Int,count)\n",
+ " request = MPI.Irecv!(rcvbuf, comm; source=2, tag=0)\n",
+ " work()\n",
+ " MPI.Wait(request)\n",
+ " @show rcvbuf\n",
+ " end\n",
+ "end\n",
+ "run(`$(mpiexec()) -np 4 julia --project=. -e $code`);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6761b4fa",
+ "metadata": {},
+ "source": [
+ "We can fix this using an `MPI_Iprobe`. It allows us to check for incoming messages without blocking.\n",
+ "\n",
+ "```julia\n",
+ "ismsg, status = MPI.Iprobe(comm, MPI.Status; source, tag)\n",
+ "```\n",
+ "```C\n",
+ "int MPI_Iprobe(int source, int tag, MPI_Comm comm, int *flag,\n",
+ " MPI_Status *status)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "75fdb2b1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "code = quote\n",
+ " using MPI\n",
+ " MPI.Init()\n",
+ " work() = sum(rand(1000))\n",
+ " comm = MPI.COMM_WORLD\n",
+ " rank = MPI.Comm_rank(comm)\n",
+ " if rank == 2\n",
+ " sleep(5) # Sleep 5 seconds\n",
+ " sndbuf = [1,2,3,5,8]\n",
+ " request = MPI.Isend(sndbuf, comm; dest=3, tag=0)\n",
+ " MPI.Wait(request)\n",
+ " end\n",
+ " if rank == 3\n",
+ " while true\n",
+ " ismsg, status = MPI.Iprobe(comm, MPI.Status; source=2, tag=0)\n",
+ " if ismsg\n",
+ " count = MPI.Get_count(status,Int)\n",
+ " rcvbuf = zeros(Int,count)\n",
+ " reqrcv = MPI.Irecv!(rcvbuf, comm; source=2, tag=0)\n",
+ " work()\n",
+ " MPI.Wait(reqrcv)\n",
+ " @show rcvbuf\n",
+ " break\n",
+ " end\n",
+ " work() # Do work while waiting for an incoming message.\n",
+ " end\n",
+ " end\n",
+ "end\n",
+ "run(`$(mpiexec()) -np 4 julia --project=. -e $code`);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c168ca8e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "? MPI.Iprobe"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6caa8d74",
+ "metadata": {},
+ "source": [
+ "## MPI Communicators\n",
+ "\n",
+ "In MPI, a **communicator** represents a group of processes that can communicate with each other. `MPI_COMM_WORLD` (`MPI.COMM_WORLD` from Julia) is a built-in communicator that represents all processes available in the MPI program. Custom communicators can also be created to group processes based on specific requirements or logical divisions. The **rank** of a processor is a unique (integer) identifier assigned to each process within a communicator. It allows processes to distinguish and address each other in communication operations.\n",
+ "\n",
+ "### Duplicating a communicator\n",
+ "\n",
+ "It is a good practice to not using the built-in communicators directly, and use a copy instead with `MPI.Comm_dup`. Different libraries using the same communicator can lead to unexpected interferences."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c87b3c82",
+ "metadata": {},
+ "source": [
+ "## Collective communication\n",
+ "\n",
+ "MPI provides collective communication functions for communication involving multiple processes. Some usual collective directives are:\n",
+ "\n",
+ "- `MPI.Gather`: Gathers data from all processes to a single process.\n",
+ "- `MPI.Scatter`: Distributes data from one process to all processes.\n",
+ "- `MPI.Bcast`: Broadcasts data from one process to all processes.\n",
+ "- `MPI.Barrier`: Synchronizes all processes.\n",
+ "\n",
+ "See more collective directives available from Julia here: https://juliaparallel.org/MPI.jl/stable/reference/collective/\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "97dc2886",
+ "metadata": {},
+ "source": [
+ "### Gather\n",
+ "\n",
+ "Each rank sends a message to the root rank (the root rank also sends a message to itself). The root rank receives all these values in a buffer (e.g. a vector)."
+ ]
+ },
+ {
+ "attachments": {
+ "g13884.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "4bdf9c02",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1f8a70c6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "code = quote\n",
+ " using MPI; MPI.Init()\n",
+ " comm = MPI.Comm_dup(MPI.COMM_WORLD)\n",
+ " nranks = MPI.Comm_size(comm)\n",
+ " rank = MPI.Comm_rank(comm)\n",
+ " root = 0\n",
+ " snd = 10*(rank+2)\n",
+ " println(\"I am sending $snd\")\n",
+ " rcv = MPI.Gather(snd,comm;root)\n",
+ " if rank == root\n",
+ " println(\"I have received: $rcv\")\n",
+ " end\n",
+ "end\n",
+ "run(`$(mpiexec()) -np 3 julia --project=. -e $code`);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8b4254d1",
+ "metadata": {},
+ "source": [
+ "### Scatter\n",
+ "\n",
+ "The root rank contains a buffer (e.g., a vector) of values (one value for each rank in a communicator). Scatter sends one value to each rank (the root rank also receives a value). The root rank can be any process in a communicator."
+ ]
+ },
+ {
+ "attachments": {
+ "g13389.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "74d5b606",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Important: Blocking directives might look simpler to use, but they can lead to dead locks if the sends and receives are not issued in the right order. Non-blocking directives can also lead to dead locks, but when waiting for the request, not when calling the send/receive functions.\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d02f935d",
+ "metadata": {},
+ "source": [
+ "## Exercises"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8e1c623",
+ "metadata": {},
+ "source": [
+ "### Exercise 1\n",
+ "\n",
+ "Implement this \"simple\" algorithm (the telephone game):\n",
+ "\n",
+ "Rank 0 generates a message (an integer). Rank 0 sends the message to rank 1. Rank 1 receives the message, increments the message by 1, and sends the result to rank 2. Rank 2 receives the message, increments the message by 1, and sends the result to rank 3. Etc. The last rank sends back the message to rank 0 closing the ring. See the next figure. Implement the communications using MPI. Do not use `Distributed`.\n"
+ ]
+ },
+ {
+ "attachments": {
+ "g5148.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "d474d781",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5e8f6e6a",
+ "metadata": {},
+ "source": [
+ "# License\n",
+ "\n",
+ "This notebook is part of the course [Programming Large Scale Parallel Systems](https://www.francescverdugo.com/XM_40017) at Vrije Universiteit Amsterdam and may be used under a [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Julia 1.10.0",
+ "language": "julia",
+ "name": "julia-1.10"
+ },
+ "language_info": {
+ "file_extension": ".jl",
+ "mimetype": "application/julia",
+ "name": "julia",
+ "version": "1.10.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/dev/julia_mpi/index.html b/dev/julia_mpi/index.html
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@@ -0,0 +1,17 @@
+
+- · XM_40017
It is not a Julia implementation of the MPI standard
+
It is a wrapper to the C interface of MPI
+
You need a C MPI installation in your system
+
+
MPI.jl provides a convenient Julia API to access MPI. For instance, this is how you get the id (rank) of the current process.
+
comm=MPI.COMM_WORLD
+rank=MPI.Comm_rank(comm)
+
+
Internally, MPI.jl uses ccall which is a mechanism that allows you to call C functions from Julia. In this, example we are calling the C function MPI_Comm_rank from the underlying MPI installation.
The Jupyter Julia kernel installed by IJulia activates the folder where the notebook is located as the default environment, which causes the main process and the worker processes to not share the same environment. Therefore, we need to set the environment as the global environment.
+
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+
In [ ]:
+
+
+
]activate
+
+
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+
+
+
+
MPI can be installed as any other Julia package using the package manager.
+
+
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+
+
In [ ]:
+
+
+
]addMPI
+
+
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+
+
+
+Note: The package you have installed it is the Julia interface to MPI, called MPI.jl. Note that it is not a MPI library by itself. It is just a thin wrapper between MPI and Julia. To use this interface, you need an actual MPI library installed in your system such as OpenMPI or MPICH. Julia downloads and installs a MPI library for you, but it is also possible to use a MPI library already available in your system. This is useful, e.g., when running on HPC clusters. See the documentation of MPI.jl for further details. See more information in https://github.com/JuliaParallel/MPI.jl
+
usingMPI
+MPI.Init()
+# Your MPI programm here
+# ...
+MPI.Finalize()
+
+
In C:
+
#include<mpi.h>
+intmain(intargc,char**argv){
+MPI_Init(NULL,NULL);
+/* Your MPI Programm here */
+MPI_Finalize();
+}
+
+
+Note: Note that the Julia syntax is almost 1-to-1 to the C one. The key difference is that in Julia MPI routines are written as `MPI.X` where in C are written `MPI_X`.
+
+
+
It is mandatory to initialize MPI before using MPI procedures.
+
In C, all processes must call MPI_Finalize before exiting.
+
In Julia, either all or none process must call MPI.Finalize().
The following cells give information about MPI processes, such as the rank id, the total number of processes and the name of the host running the code respectively. Before calling this functions one needs to initialize MPI.
+
+
+
+
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+
+
In [ ]:
+
+
+
usingMPI
+MPI.Init()
+
+
+
+
+
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+
+
+
+
+
+
+
Current rank (process) id
+
+
+
+
+
+
+
+
+
In [ ]:
+
+
+
comm=MPI.COMM_WORLD
+rank=MPI.Comm_rank(comm)
+
+
+
+
+
+
+
+
+
+
+
+
+
Number of available processes
+
+
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+
In [ ]:
+
+
+
nranks=MPI.Comm_size(comm)
+
+
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+
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+
+
+
+
+
Name of the current host
+
+
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+
In [ ]:
+
+
+
MPI.Get_processor_name()
+
+
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+
+
+
+
+
Note that this note notebook is not running with different MPI processes (yet). So using MPI will only make sense later when we add more processes.
Using these functions we can create the a classic MPI hello world example. This example (or variations thereof) is used in practice to check how MPI ranks are mapped to available processing units in a given computing system.
+
+
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+
+
+
+
In [ ]:
+
+
+
usingMPI
+MPI.Init()
+comm=MPI.COMM_WORLD
+nranks=MPI.Comm_size(comm)
+rank=MPI.Comm_rank(comm)
+host=MPI.Get_processor_name()
+println("Hello from $host, I am process $rank of $nranks processes!")
+
Julia code typically needs to be in a file to run it in with MPI. Let's us write our hello world in a file:
+
+
+
+
+
+
+
+
+
In [ ]:
+
+
+
code=raw"""
+using MPI
+MPI.Init()
+comm = MPI.COMM_WORLD
+nranks = MPI.Comm_size(comm)
+rank = MPI.Comm_rank(comm)
+println("Hello, I am process $rank of $nranks processes!")
+MPI.Finalize()
+"""
+filename=tempname()*".jl"
+write(filename,code);
+
+
+
+
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+
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+
+
+
+
Now, we can run it
+
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In [ ]:
+
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+
usingMPI
+run(`$(mpiexec()) -np 4 julia --project=. $filename`);
+
+
+
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+
+
+
+
+
+Note: Function `mpiexec` provided by `MPI.jl` is a convenient way of accessing the `mpiexec` program that matches the MPI installation used my Julia.
+
In the Hello world example above we have created an auxiliary file to run the code with MPI. This can be annoying specially if you are working in a jupyter notebook. With this other syntax you can skip creating the auxiliary file. we use quote which is part of the meta-programming capabilities of Julia.
+
+
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+
+
+
In [ ]:
+
+
+
code=quote
+usingMPI
+MPI.Init()
+comm=MPI.COMM_WORLD
+nranks=MPI.Comm_size(comm)
+rank=MPI.Comm_rank(comm)
+println("Hello, I am process $rank of $nranks processes!")
+MPI.Finalize()
+end
+run(`$(mpiexec()) -np 4 julia --project=. -e $code`);
+
Note that mpiexec creates new processes which are different from the process running this notebook. In particular, these new processes will not see any variables or function definitions in the current notebook. So, the full MPI program needs to be in the source file passed to Julia or the quote block.
So, the full MPI program needs to be in the source file passed to Julia or the quote block. In practice, long MPI programms are written as Julia packages using several files, which are then loaded by each MPI process.
MPI provides point-to-point communication directives for arbitrary communication between processes. Point-to-point communications are two-sided: there is a sender and a receiver. Here, we will discuss these basic directives:
+
+
MPI_Send, and MPI_Recv! (blocking directives)
+
MPI_Isend, and MPI_Irecv! (non-blocking directives, aka incomplete directives)
+
MPI_Bsend, MPI_Ssend, and MPI_Rsend (advanced communication modes)
When using MPI.ANY_SOURCE and MPI.ANY_TAG it might be still useful to know which was the sender and which tag was used. This information is given by a MPI.Status object.
+
_,status=MPI.Recv!(rcvbuf,comm,MPI.Status;source,tag)
+status.source# Gives the source
+status.tag# Gives the tag
+
Note that we need to provide a receive buffer with the right size, but it general we might do not know which is the size of the incoming message. This can be solved using an MPI_Probe. It works similar to MPI_Recv, but instead of receiving the message only receives information about the message (source, tag, and also message size).
We can get the message size from the status object using function MPI_Get_count. We can also get the source and tag from the status object as shown before.
It is safe to re-write the send buffer once MPI_Send returns.
+
The received message is guaranteed to be fully available in the receive buffer once MPI_Recv returns.
+
+
+
+
+
+
+
+
+
+
In [ ]:
+
+
+
code=quote
+usingMPI
+MPI.Init()
+comm=MPI.COMM_WORLD
+rank=MPI.Comm_rank(comm)
+ifrank==2
+sndbuf=[1,2,3,5,8]
+MPI.Send(sndbuf,comm;dest=3,tag=0)
+sndbuf.=0# This is fine. Send has returned.
+end
+ifrank==3
+rcvbuf=zeros(Int,5)
+MPI.Recv!(rcvbuf,comm,MPI.Status;source=2,tag=0)
+# recvbuf will have the incomming message fore sure. Recv! has returned.
+@showrcvbuf
+end
+end
+run(`$(mpiexec()) -np 4 julia --project=. -e $code`);
+
+
+
+
+
+
+
+
+
+
+
+
+
However:
+
+
We cannot assume synchronization between sender and receiver. I.e., blocking is not the same as synchronous. We cannot assume that a receive has been posted once MPI_Send returns as the underlying implementation might copy the send message into an internal buffer and return before any matching MPI_Recv started.
+
+
A blocking send is not synchronous, but we cannot assume that it is asynchronous. The underlying implementation might not use any auxiliary buffer and wait for a matching receive. Assuming buffering is erroneous and can lead to dead locks.
The following program will or will not work depending whether the underlying implementation uses buffering. On my laptop, it works with n=1, but leads to a dead lock when n=10000. The MPI implementation decided to buffer or not depending on the message size.
We can fix the program by smartly ordering the sends and the receives. This should work for any value of n as long as we have enough memory in the system.
In all cases, it is safe to reuse the send buffer once the corresponding send returns. I.e., all the following sends are complete MPI operations. However, there are some important differences.
It may be started only if the matching receive is already posted.
+
Erroneous if there is no matching receive yet.
+
Otherwise, same as an MPI_Ssend.
+
+
All these send types are matched with a MPI_Recv. I.e., there is no MPI_Brecv, MPI_Srecv, MPI_Rrecv. For further information about the communication modes, refer to this section of the MPI standard.
+
+Note: `MPI_Bsend`, `MPI_Ssend`, and `MPI_Rsend` are not exposed in the Julia bindings via a high-level interface like for `MPI.Send`, but they can be accessed using the low-level bindings in the submodule `MPI.API` (not shown in this notebook).
+
This program in incorrect both on the send and the receive side.
+
+
One needs to wait for completion before reseting the send buffer
+
One needs to wait for completion before using the receive buffer
+
+
+
+
+
+
+
+
+
+
In [ ]:
+
+
+
code=quote
+usingMPI
+MPI.Init()
+comm=MPI.COMM_WORLD
+rank=MPI.Comm_rank(comm)
+ifrank==2
+sndbuf=[1,2,3,5,8]
+request=MPI.Isend(sndbuf,comm;dest=3,tag=0)
+sndbuf.=10# We cannot set the sndbuf before MPI.Wait.
+MPI.Wait(request)
+end
+ifrank==3
+rcvbuf=zeros(Int,5)
+request=MPI.Irecv!(rcvbuf,comm;source=2,tag=0)
+@showrcvbuf# Not guaranteed to have the correct value.
+MPI.Wait(request)
+end
+end
+run(`$(mpiexec()) -np 4 julia --project=. -e $code`);
+
If we use MPI_Probe we miss the opportunity to do local work before a send is started.
+
+
+
+
+
+
+
+
+
In [ ]:
+
+
+
code=quote
+usingMPI
+MPI.Init()
+work()=sum(rand(1000))
+comm=MPI.COMM_WORLD
+rank=MPI.Comm_rank(comm)
+ifrank==2
+sleep(5)# Sleep 5 seconds
+sndbuf=[1,2,3,5,8]
+request=MPI.Isend(sndbuf,comm;dest=3,tag=0)
+MPI.Wait(request)
+end
+ifrank==3
+# We are going to wait here for about 5 seconds
+# Missing the opportunity to do some useful work
+status=MPI.Probe(comm,MPI.Status;source=2,tag=0)
+count=MPI.Get_count(status,Int)
+rcvbuf=zeros(Int,count)
+request=MPI.Irecv!(rcvbuf,comm;source=2,tag=0)
+work()
+MPI.Wait(request)
+@showrcvbuf
+end
+end
+run(`$(mpiexec()) -np 4 julia --project=. -e $code`);
+
+
+
+
+
+
+
+
+
+
+
+
+
We can fix this using an MPI_Iprobe. It allows us to check for incoming messages without blocking.
In MPI, a communicator represents a group of processes that can communicate with each other. MPI_COMM_WORLD (MPI.COMM_WORLD from Julia) is a built-in communicator that represents all processes available in the MPI program. Custom communicators can also be created to group processes based on specific requirements or logical divisions. The rank of a processor is a unique (integer) identifier assigned to each process within a communicator. It allows processes to distinguish and address each other in communication operations.
It is a good practice to not using the built-in communicators directly, and use a copy instead with MPI.Comm_dup. Different libraries using the same communicator can lead to unexpected interferences.
Each rank sends a message to the root rank (the root rank also sends a message to itself). The root rank receives all these values in a buffer (e.g. a vector).
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
In [ ]:
+
+
+
code=quote
+usingMPI;MPI.Init()
+comm=MPI.Comm_dup(MPI.COMM_WORLD)
+nranks=MPI.Comm_size(comm)
+rank=MPI.Comm_rank(comm)
+root=0
+snd=10*(rank+2)
+println("I am sending $snd")
+rcv=MPI.Gather(snd,comm;root)
+ifrank==root
+println("I have received: $rcv")
+end
+end
+run(`$(mpiexec()) -np 3 julia --project=. -e $code`);
+
The root rank contains a buffer (e.g., a vector) of values (one value for each rank in a communicator). Scatter sends one value to each rank (the root rank also receives a value). The root rank can be any process in a communicator.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
In [ ]:
+
+
+
code=quote
+usingMPI;MPI.Init()
+comm=MPI.Comm_dup(MPI.COMM_WORLD)
+nranks=MPI.Comm_size(comm)
+rank=MPI.Comm_rank(comm)
+root=0
+rcv=Ref(0)
+ifrank==root
+snd=[10*(i+1)foriin1:nranks]
+println("I am sending: $snd")
+else
+snd=nothing
+end
+MPI.Scatter!(snd,rcv,comm;root)
+println("I have received: $(rcv[])")
+end
+run(`$(mpiexec()) -np 3 julia --project=. -e $code`);
+
MPI also provides point-to-point communication directives for arbitrary communication between processes. Point-to-point communications are two-sided: there is a sender and a receiver. Here, we will discuss these basic directives:
+
+
MPI.Isend, and MPI.Irecv! (non-blocking directives)
+
MPI.Send, and MPI.Recv! (blocking directives)
+
+
Non-blocking directives return immediately and return an MPI.Request object. This request object can be queried with functions like MPI.Wait. It is mandatory to wait on the request object before reading the receive buffer, or before writing again on the send buffer.
+
For blocking directives, it is save to read/write from/to the receive/send buffer once the function has returned. By default, blocking directives might wait (or might not wait) for a matching send/receive.
+For fine control, MPI offers advanced blocking directives with different blocking behaviors (called communication modes, see section 3.9 of the MPI standard 4.0). Blocking communication will be discussed later in the course.
If we start a receive before a matching send, we will block in the call to MPI.Recv!. Run the next cell and note that the message is not printed since the process is blocked at MPI.Recv! waiting for a matching send.
+
+
+
+
+
+
+
+
+
In [ ]:
+
+
+
@spawnat4begin
+buffer=Ref(0)
+comm=MPI.COMM_WORLD
+MPI.Recv!(buffer,comm;source=2-2,tag=0)
+println("I have received $(buffer[]).")
+end;
+
+
+
+
+
+
+
+
+
+
+
+
+
If you run the next cell containing the corresponding send, the communication will take place.
+
+
+
+
+
+
+
+
+
In [ ]:
+
+
+
@spawnat2begin
+buffer=Ref(2)
+comm=MPI.COMM_WORLD
+MPI.Send(buffer,comm;dest=4-2,tag=0)
+println("I have send $(buffer[]). It is now safe to overwite the buffer.")
+end;
+
MPI blocks without yielding (we cannot switch to other Julia tasks). Run next cell:
+
+
+
+
+
+
+
+
+
In [ ]:
+
+
+
@spawnat4begin
+buffer=Ref(0)
+comm=MPI.COMM_WORLD
+MPI.Recv!(buffer,comm;source=2-2,tag=0)
+println("I have received $(buffer[]).")
+end;
+
+
+
+
+
+
+
+
+
+
+
+
+
Now try to spawn other tasks on process 4 by running next cell. This task will not be served yet.
+
+
+
+
+
+
+
+
+
In [ ]:
+
+
+
@spawnat4println("Hello!");
+
+
+
+
+
+
+
+
+
+
+
+
+
We first need to unlock the receive with a matching send. Then the task printing "Hello!" will be finally served.
+
+
+
+
+
+
+
+
+
In [ ]:
+
+
+
@spawnat2begin
+buffer=Ref(2)
+comm=MPI.COMM_WORLD
+MPI.Send(buffer,comm;dest=4-2,tag=0)
+println("I have send $(buffer[]). It is now safe to overwite the buffer.")
+end;
+
@spawnat4begin
+buffer=Ref(0)
+comm=MPI.COMM_WORLD
+req=MPI.Irecv!(buffer,comm;source=2-2,tag=0)
+println("Not yet safe to read the buffer")
+MPI.Wait(req)
+println("I have received $(buffer[]).")
+end;
+
+
+
+
+
+
+
+
+
+
+
In [ ]:
+
+
+
@spawnat2begin
+buffer=Ref(2)
+comm=MPI.COMM_WORLD
+req=MPI.Isend(buffer,comm;dest=4-2,tag=0)
+println("Not yet safe to write the buffer")
+MPI.Wait(req)
+println("I have send $(buffer[]). It is now safe to overwite the buffer.")
+end;
+
The first rank generates a message and sends it to the last rank. The last rank receives the message and multiplies it by a coefficient. The last rank sends the result back to the first rank.
@everywhereworkers()begin
+comm=MPI.Comm_dup(MPI.COMM_WORLD)
+rank=MPI.Comm_rank(comm)
+nranks=MPI.Comm_size(comm)
+snder=0
+rcver=nranks-1
+buffer=Ref(0)
+ifrank==snder
+msg=10*(rank+2)
+println("I am sending: $msg")
+buffer[]=msg
+MPI.Send(buffer,comm;dest=rcver,tag=0)
+MPI.Recv!(buffer,comm,source=rcver,tag=0)
+msg=buffer[]
+println("I have received: $msg")
+end
+ifrank==rcver
+MPI.Recv!(buffer,comm,source=snder,tag=0)
+msg=buffer[]
+println("I have received: $msg")
+coef=(rank+2)
+msg=msg*coef
+println("I am sending: $msg")
+buffer[]=msg
+MPI.Send(buffer,comm;dest=snder,tag=0)
+end
+end
+
+
+
+
+
+
+
+
+
+
+
+
+
+Important: Blocking directives might look simpler to use, but they can lead to dead locks if the sends and receives are not issued in the right order. Non-blocking directives can also lead to dead locks, but when waiting for the request, not when calling the send/receive functions.
+
Implement this "simple" algorithm (the telephone game):
+
Rank 0 generates a message (an integer). Rank 0 sends the message to rank 1. Rank 1 receives the message, increments the message by 1, and sends the result to rank 2. Rank 2 receives the message, increments the message by 1, and sends the result to rank 3. Etc. The last rank sends back the message to rank 0 closing the ring. See the next figure. Implement the communications using MPI. Do not use Distributed.
This document was generated with Documenter.jl version 1.1.1 on Monday 16 October 2023. Using Julia version 1.9.3.
+
Settings
This document was generated with Documenter.jl version 1.5.0 on Monday 19 August 2024. Using Julia version 1.10.4.
diff --git a/dev/julia_tutorial_src/index.html b/dev/julia_tutorial_src/index.html
index eaca23a..302ed23 100644
--- a/dev/julia_tutorial_src/index.html
+++ b/dev/julia_tutorial_src/index.html
@@ -7333,11 +7333,12 @@ a.anchor-link {
if (!diagrams.length) {
return;
}
- const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.5.0/mermaid.esm.min.mjs")).default;
+ const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.7.0/mermaid.esm.min.mjs")).default;
const parser = new DOMParser();
mermaid.initialize({
maxTextSize: 100000,
+ maxEdges: 100000,
startOnLoad: false,
fontFamily: window
.getComputedStyle(document.body)
@@ -7408,7 +7409,8 @@ a.anchor-link {
let results = null;
let output = null;
try {
- const { svg } = await mermaid.render(id, raw, el);
+ let { svg } = await mermaid.render(id, raw, el);
+ svg = cleanMermaidSvg(svg);
results = makeMermaidImage(svg);
output = document.createElement("figure");
results.map(output.appendChild, output);
@@ -7423,6 +7425,38 @@ a.anchor-link {
parent.appendChild(output);
}
+
+ /**
+ * Post-process to ensure mermaid diagrams contain only valid SVG and XHTML.
+ */
+ function cleanMermaidSvg(svg) {
+ return svg.replace(RE_VOID_ELEMENT, replaceVoidElement);
+ }
+
+
+ /**
+ * A regular expression for all void elements, which may include attributes and
+ * a slash.
+ *
+ * @see https://developer.mozilla.org/en-US/docs/Glossary/Void_element
+ *
+ * Of these, only ` ` is generated by Mermaid in place of `\n`,
+ * but _any_ "malformed" tag will break the SVG rendering entirely.
+ */
+ const RE_VOID_ELEMENT =
+ /<\s*(area|base|br|col|embed|hr|img|input|link|meta|param|source|track|wbr)\s*([^>]*?)\s*>/gi;
+
+ /**
+ * Ensure a void element is closed with a slash, preserving any attributes.
+ */
+ function replaceVoidElement(match, tag, rest) {
+ rest = rest.trim();
+ if (!rest.endsWith('/')) {
+ rest = `${rest} /`;
+ }
+ return `<${tag} ${rest}>`;
+ }
+
void Promise.all([...diagrams].map(renderOneMarmaid));
});
diff --git a/dev/matrix_matrix.ipynb b/dev/matrix_matrix.ipynb
index 1685c7d..7efc0dc 100644
--- a/dev/matrix_matrix.ipynb
+++ b/dev/matrix_matrix.ipynb
@@ -219,10 +219,10 @@
"metadata": {},
"source": [
"
\n",
- "Note: The matrix-matrix multiplication naively implemented with 3 nested loops as above is known to be very inefficient (memory bound). Libraries such as BLAS provide much more efficient implementations, which are the ones used in practice (e.g., by the `*` operator in Julia). We consider, our hand-written implementation as a simple way of expressing the algorithm we are interested in.\n",
+ "Note: The matrix-matrix multiplication naively implemented with 3 nested loops as above is known to be very inefficient (memory bound). Libraries such as BLAS provide much more efficient implementations, which are the ones used in practice (e.g., by the `*` operator in Julia). We consider our hand-written implementation as a simple way of expressing the algorithm we are interested in.\n",
"
\n",
"\n",
- "Run the following cell to compare the performance of our hand-written function with respect to the built in function `mul!`\n"
+ "Run the following cell to compare the performance of our hand-written function with respect to the built in function `mul!`.\n"
]
},
{
@@ -1060,107 +1060,6 @@
"println(\"Efficiency = \", 100*(T1/TP)/P, \"%\")"
]
},
- {
- "cell_type": "markdown",
- "id": "fa8d7f40",
- "metadata": {},
- "source": [
- "### Exercise 2"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0e7c607e",
- "metadata": {},
- "source": [
- "The implementation of algorithm 1 is very impractical. One needs as many processors as entries in the result matrix C. For 1000 times 1000 matrix one would need a supercomputer with one million processes! We can easily fix this problem by using less processors and spawning the computation of an entry in any of the available processes.\n",
- "See the following code:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "023b20d1",
- "metadata": {},
- "outputs": [],
- "source": [
- "function matmul_dist_1_v2!(C, A, B)\n",
- " m = size(C,1)\n",
- " n = size(C,2)\n",
- " l = size(A,2)\n",
- " @assert size(A,1) == m\n",
- " @assert size(B,2) == n\n",
- " @assert size(B,1) == l\n",
- " z = zero(eltype(C))\n",
- " @sync for j in 1:n\n",
- " for i in 1:m\n",
- " Ai = A[i,:]\n",
- " Bj = B[:,j]\n",
- " ftr = @spawnat :any begin\n",
- " Cij = z\n",
- " for k in 1:l\n",
- " @inbounds Cij += Ai[k]*Bj[k]\n",
- " end\n",
- " Cij\n",
- " end\n",
- " @async C[i,j] = fetch(ftr)\n",
- " end\n",
- " end\n",
- " C\n",
- "end"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "52005ca1",
- "metadata": {},
- "source": [
- "With this new implementation, we can multiply matrices of arbitrary size with a fixed number of workers. Test it:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c1d3595b",
- "metadata": {},
- "outputs": [],
- "source": [
- "using Test\n",
- "N = 50\n",
- "A = rand(N,N)\n",
- "B = rand(N,N)\n",
- "C = similar(A)\n",
- "@test matmul_dist_1_v2!(C,A,B) ≈ A*B"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ab609c18",
- "metadata": {},
- "source": [
- "Run the next cell to check the performance of this implementation. Note that we are far away from the optimal speed up. Why? To answer this question compute the theoretical communication over computation ratio for this implementation and reason about the obtained result. Hint: the number of times a worker is spawned in this implementation is N^2/P on average."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d7d31710",
- "metadata": {},
- "outputs": [],
- "source": [
- "N = 100\n",
- "A = rand(N,N)\n",
- "B = rand(N,N)\n",
- "C = similar(A)\n",
- "P = nworkers()\n",
- "T1 = @belapsed matmul_seq!(C,A,B)\n",
- "C = similar(A)\n",
- "TP = @belapsed matmul_dist_1_v2!(C,A,B)\n",
- "println(\"Speedup = \", T1/TP)\n",
- "println(\"Optimal speedup = \", P)\n",
- "println(\"Efficiency = \", 100*(T1/TP)/P, \"%\")"
- ]
- },
{
"cell_type": "markdown",
"id": "8e171362",
@@ -1175,15 +1074,15 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Julia 1.9.0",
+ "display_name": "Julia 1.10.0",
"language": "julia",
- "name": "julia-1.9"
+ "name": "julia-1.10"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
- "version": "1.9.0"
+ "version": "1.10.0"
}
},
"nbformat": 4,
diff --git a/dev/matrix_matrix/index.html b/dev/matrix_matrix/index.html
index a37ea50..d235dc9 100644
--- a/dev/matrix_matrix/index.html
+++ b/dev/matrix_matrix/index.html
@@ -1,5 +1,5 @@
-Matrix-matrix multiplication · XM_40017
This document was generated with Documenter.jl version 1.1.1 on Monday 16 October 2023. Using Julia version 1.9.3.
+
Settings
This document was generated with Documenter.jl version 1.5.0 on Monday 19 August 2024. Using Julia version 1.10.4.
diff --git a/dev/matrix_matrix_src/index.html b/dev/matrix_matrix_src/index.html
index aa284d3..baac04b 100644
--- a/dev/matrix_matrix_src/index.html
+++ b/dev/matrix_matrix_src/index.html
@@ -7333,11 +7333,12 @@ a.anchor-link {
if (!diagrams.length) {
return;
}
- const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.5.0/mermaid.esm.min.mjs")).default;
+ const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.7.0/mermaid.esm.min.mjs")).default;
const parser = new DOMParser();
mermaid.initialize({
maxTextSize: 100000,
+ maxEdges: 100000,
startOnLoad: false,
fontFamily: window
.getComputedStyle(document.body)
@@ -7408,7 +7409,8 @@ a.anchor-link {
let results = null;
let output = null;
try {
- const { svg } = await mermaid.render(id, raw, el);
+ let { svg } = await mermaid.render(id, raw, el);
+ svg = cleanMermaidSvg(svg);
results = makeMermaidImage(svg);
output = document.createElement("figure");
results.map(output.appendChild, output);
@@ -7423,6 +7425,38 @@ a.anchor-link {
parent.appendChild(output);
}
+
+ /**
+ * Post-process to ensure mermaid diagrams contain only valid SVG and XHTML.
+ */
+ function cleanMermaidSvg(svg) {
+ return svg.replace(RE_VOID_ELEMENT, replaceVoidElement);
+ }
+
+
+ /**
+ * A regular expression for all void elements, which may include attributes and
+ * a slash.
+ *
+ * @see https://developer.mozilla.org/en-US/docs/Glossary/Void_element
+ *
+ * Of these, only ` ` is generated by Mermaid in place of `\n`,
+ * but _any_ "malformed" tag will break the SVG rendering entirely.
+ */
+ const RE_VOID_ELEMENT =
+ /<\s*(area|base|br|col|embed|hr|img|input|link|meta|param|source|track|wbr)\s*([^>]*?)\s*>/gi;
+
+ /**
+ * Ensure a void element is closed with a slash, preserving any attributes.
+ */
+ function replaceVoidElement(match, tag, rest) {
+ rest = rest.trim();
+ if (!rest.endsWith('/')) {
+ rest = `${rest} /`;
+ }
+ return `<${tag} ${rest}>`;
+ }
+
void Promise.all([...diagrams].map(renderOneMarmaid));
});
@@ -7742,9 +7776,9 @@ a.anchor-link {
-Note: The matrix-matrix multiplication naively implemented with 3 nested loops as above is known to be very inefficient (memory bound). Libraries such as BLAS provide much more efficient implementations, which are the ones used in practice (e.g., by the `*` operator in Julia). We consider, our hand-written implementation as a simple way of expressing the algorithm we are interested in.
+Note: The matrix-matrix multiplication naively implemented with 3 nested loops as above is known to be very inefficient (memory bound). Libraries such as BLAS provide much more efficient implementations, which are the ones used in practice (e.g., by the `*` operator in Julia). We consider our hand-written implementation as a simple way of expressing the algorithm we are interested in.
-
Run the following cell to compare the performance of our hand-written function with respect to the built in function mul!
+
Run the following cell to compare the performance of our hand-written function with respect to the built in function mul!.
@@ -8757,131 +8791,6 @@ d) O(N²/P) communication and O(N³/P) computation
The implementation of algorithm 1 is very impractical. One needs as many processors as entries in the result matrix C. For 1000 times 1000 matrix one would need a supercomputer with one million processes! We can easily fix this problem by using less processors and spawning the computation of an entry in any of the available processes.
-See the following code:
Run the next cell to check the performance of this implementation. Note that we are far away from the optimal speed up. Why? To answer this question compute the theoretical communication over computation ratio for this implementation and reason about the obtained result. Hint: the number of times a worker is spawned in this implementation is N^2/P on average.
This document was generated with Documenter.jl version 1.1.1 on Monday 16 October 2023. Using Julia version 1.9.3.
+
Settings
This document was generated with Documenter.jl version 1.5.0 on Monday 19 August 2024. Using Julia version 1.10.4.
diff --git a/dev/notebook-hello_src/index.html b/dev/notebook-hello_src/index.html
index b68e9f5..8372f8c 100644
--- a/dev/notebook-hello_src/index.html
+++ b/dev/notebook-hello_src/index.html
@@ -7333,11 +7333,12 @@ a.anchor-link {
if (!diagrams.length) {
return;
}
- const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.5.0/mermaid.esm.min.mjs")).default;
+ const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.7.0/mermaid.esm.min.mjs")).default;
const parser = new DOMParser();
mermaid.initialize({
maxTextSize: 100000,
+ maxEdges: 100000,
startOnLoad: false,
fontFamily: window
.getComputedStyle(document.body)
@@ -7408,7 +7409,8 @@ a.anchor-link {
let results = null;
let output = null;
try {
- const { svg } = await mermaid.render(id, raw, el);
+ let { svg } = await mermaid.render(id, raw, el);
+ svg = cleanMermaidSvg(svg);
results = makeMermaidImage(svg);
output = document.createElement("figure");
results.map(output.appendChild, output);
@@ -7423,6 +7425,38 @@ a.anchor-link {
parent.appendChild(output);
}
+
+ /**
+ * Post-process to ensure mermaid diagrams contain only valid SVG and XHTML.
+ */
+ function cleanMermaidSvg(svg) {
+ return svg.replace(RE_VOID_ELEMENT, replaceVoidElement);
+ }
+
+
+ /**
+ * A regular expression for all void elements, which may include attributes and
+ * a slash.
+ *
+ * @see https://developer.mozilla.org/en-US/docs/Glossary/Void_element
+ *
+ * Of these, only ` ` is generated by Mermaid in place of `\n`,
+ * but _any_ "malformed" tag will break the SVG rendering entirely.
+ */
+ const RE_VOID_ELEMENT =
+ /<\s*(area|base|br|col|embed|hr|img|input|link|meta|param|source|track|wbr)\s*([^>]*?)\s*>/gi;
+
+ /**
+ * Ensure a void element is closed with a slash, preserving any attributes.
+ */
+ function replaceVoidElement(match, tag, rest) {
+ rest = rest.trim();
+ if (!rest.endsWith('/')) {
+ rest = `${rest} /`;
+ }
+ return `<${tag} ${rest}>`;
+ }
+
void Promise.all([...diagrams].map(renderOneMarmaid));
});
diff --git a/dev/objects.inv b/dev/objects.inv
new file mode 100644
index 0000000..09dc821
Binary files /dev/null and b/dev/objects.inv differ
diff --git a/dev/pdes.ipynb b/dev/pdes.ipynb
index 197440d..ad0144e 100644
--- a/dev/pdes.ipynb
+++ b/dev/pdes.ipynb
@@ -28,6 +28,38 @@
"- Distributed sparse matrix-vector product"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "a343cca6",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Note: Do not forget to execute the cell below before starting this notebook! \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "821ac5e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "using Printf\n",
+ "function answer_checker(answer,solution)\n",
+ " if answer == solution\n",
+ " \"🥳 Well done! \"\n",
+ " else\n",
+ " \"It's not correct. Keep trying! 💪\"\n",
+ " end |> println\n",
+ "end\n",
+ "pdes_check_0(answer)=answer_checker(answer,\"f\")\n",
+ "pdes_check_1(answer)=answer_checker(answer, \"b\")\n",
+ "pdes_check_2(answer)=answer_checker(answer, \"c\")\n",
+ "pdes_check_3(answer)=answer_checker(answer, \"d\")\n",
+ "pdes_check_4(answer)=answer_checker(answer, \"a\")"
+ ]
+ },
{
"cell_type": "markdown",
"id": "da4d45cd",
@@ -35,7 +67,7 @@
"source": [
"## Mini project\n",
"\n",
- "- Simulating the temperature distribution in a closed room"
+ "For the demonstration of different numberical methods we will use the example project of simulating the temperature distribution in a closed room. "
]
},
{
@@ -45,9 +77,9 @@
"source": [
"### Problem statement\n",
"\n",
- "- Given the temperature at the boundary of a room (walls, window, heater)\n",
- "- Predict the temperature at any point of the room\n",
- "- Compute it in parallel"
+ "Given the temperature at the boundary of a room, predict the temperature at any point in the room. The illustration below shows the temperature at different intervals of the room boundary (including a window and a heater). \n",
+ "\n",
+ "We will discuss the serial implementation first and learn how to compute it in parallel afterwards."
]
},
{
@@ -72,7 +104,10 @@
"source": [
"### Laplace equation\n",
"\n",
- "$\\dfrac{\\partial^2 u(x,y)}{\\partial x^2} + \\dfrac{\\partial^2 u(x,y)}{\\partial y^2} = 0$\n"
+ "Assuming that the room is isolated, it can be shown that the temperature in the room can be described using the [Laplace equation](https://en.wikipedia.org/wiki/Laplace%27s_equation)\n",
+ "\n",
+ "\n",
+ "$$\\dfrac{\\partial^2 u(x,y)}{\\partial x^2} + \\dfrac{\\partial^2 u(x,y)}{\\partial y^2} = 0.$$\n"
]
},
{
@@ -132,13 +167,15 @@
"source": [
"### Numerical methods for PDEs\n",
"\n",
+ "There are several methods for solving PDEs: \n",
+ "\n",
"- Finite difference method (FDM)\n",
"- Finite element method (FEM)\n",
"- Finite volume method (FVM)\n",
"- Boundary element method (BEM)\n",
"- Meshfree methods\n",
"\n",
- "Main idea: Transform a PDE into a system of algebraic equations.\n"
+ "All of them follow the same main idea: to transform a PDE into a system of linear equations. The problem is first discretized into a computational mesh. The solution to the PDE at any point of the mesh is then given by the solution for vector x. \n"
]
},
{
@@ -164,8 +201,7 @@
"source": [
"### Finite Difference method\n",
"\n",
- " - Pro: Easy to implement and computationally efficient\n",
- " - Con: Difficult to handle complex geometries\n"
+ "To solve the temperature distribution in the room, we choose the Finite Difference method. The advantage of this method is that it is easy to implement and computationally efficient. On the other hand, it is not suitable for more complex geometries. "
]
},
{
@@ -188,9 +224,9 @@
"id": "54a8f290",
"metadata": {},
"source": [
- "- Goal: Compute the temperature at each grid point\n",
- "- The (unknown) values can be stored in a computer using an array\n",
- "- `u[i,j]` is the temperature at point $(i,j)$"
+ "With the finite difference method, we first model the room as a grid, which can be stored using an array. Each entry of the matrix `u[i,j]` represents the temperature in the room at point $(i,j)$. The goal is now to compute the temperature at each grid point. \n",
+ "\n",
+ "Let's set up the grid matrix for our problem."
]
},
{
@@ -261,7 +297,9 @@
"id": "53231e30",
"metadata": {},
"source": [
- "### Data Visualization"
+ "### Data Visualization\n",
+ "\n",
+ "To illustrate the solution, we also write a visualization function that plots the temperature of the room as a heatmap. "
]
},
{
@@ -307,31 +345,20 @@
},
{
"cell_type": "markdown",
- "id": "8e683fe1",
+ "id": "7f7a0f7f",
"metadata": {},
"source": [
"### How to find the temperature at the interior points?\n",
"\n",
- "- We have an unknown for each interior point\n",
- "- We need an equation for each unknown\n",
- "- How to define these equations?\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7f7a0f7f",
- "metadata": {},
- "source": [
- "### Finite Difference stencil\n",
+ "To set up a system of linear equations, we have to find one equation for each unknown value in the grid. The Laplace equation can be approximated by the following linear equation:\n",
"\n",
+ "$$\\dfrac{\\partial^2 u}{\\partial x^2} + \\dfrac{\\partial^2 u}{\\partial y^2} = 0 $$\n",
"\n",
+ "$$\\downarrow$$\n",
"\n",
- "$\\dfrac{\\partial^2 u}{\\partial x^2} + \\dfrac{\\partial^2 u}{\\partial y^2} = 0 $\n",
+ "$$u_{i-1,j} + u_{i+1,j} + u_{i,j-1} + u_{i,j+1} - 4u_{i,j} = 0.$$\n",
"\n",
- "$\\downarrow$\n",
- "\n",
- "$u_{i-1,j} + u_{i+1,j} + u_{i,j-1} + u_{i,j+1} - 4u_{i,j} = 0$\n",
- "\n"
+ "This approximation computes the value at point $(i,j)$ by combining the values of its neighbors. \n"
]
},
{
@@ -352,15 +379,44 @@
},
{
"cell_type": "markdown",
- "id": "2a380c04",
+ "id": "406f9436",
"metadata": {},
"source": [
"### System of linear equations\n",
" \n",
- " - At each interior point we have an equation\n",
- " - All these equations can be arranged in array form\n",
+ "We can now obtain this kind of equation for every unknown point in the grid (= all the interior points). All these equations can be arranged in array form\n",
" \n",
- "$Ax=b$"
+ "$$Ax=b.$$\n",
+ "\n",
+ "
\n",
+ "Question: For a grid of size NxN, how many rows does matrix A have? \n",
+ "
\n",
+ "\n",
+ " a) N\n",
+ " b) N²\n",
+ " c) N-1\n",
+ " d) (N-1)x(N-1)\n",
+ " e) N-2\n",
+ " f) (N-2)x(N-2)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b52c8b2f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "answer=\"x\" # Replace x with a,b,c,d,e, or f\n",
+ "pdes_check_0(answer)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2a380c04",
+ "metadata": {},
+ "source": [
+ "Next we create the matrix $A$: "
]
},
{
@@ -421,6 +477,16 @@
"A"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "1f4c98fd",
+ "metadata": {},
+ "source": [
+ "We created the matrix $A$ of the system of linear equations $Ax=b$ for a grid size $N=5$. For a grid of this size, we need 9 linear equations since there are 9 interior points. Note that we are using two types of numeration: one that defines the coordinates in the grid, e.g. $(2,3)$. The other numeration simply enumerates the interior values row-wise (1,2,...,9). The latter numeration is used to construct the matrix $A$. \n",
+ "\n",
+ "For any grid size, the equations matrix will only contain up to 5 nonzero values per row. Therefore, it is useful to store it as a sparse array which only stores the nonzero values under the hood. This is essential for the implementation because a lot of memory can be saved in this way. "
+ ]
+ },
{
"cell_type": "markdown",
"id": "7e521df4",
@@ -453,8 +519,40 @@
"\n",
"Two possible solution methods have already been discussed in the course:\n",
"\n",
- "- Jacobi\n",
- "- Gaussian elimination"
+ "- Jacobi method\n",
+ "- Gaussian elimination\n",
+ "\n",
+ "To determine wether any of these methods are useful solvers for our problem, we analyze if they are _algorithmically scalable_. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f29c756a",
+ "metadata": {},
+ "source": [
+ "### Algorithmically scalable solver\n",
+ "\n",
+ "A computation method is an _algorithmically scalable_ solver if the total cost scales at most linearly with respect to the total problem size.\n",
+ "\n",
+ "
\n",
+ "Question: Let $R$ be our problem size, the number of rows in our matrix $A$. Because we have $N$ points in each dimension of the grid, $R=O(N^2)$. What is the complexity of the Jacobi method and Gaussian elimination with respect to the problem size $R$?\n",
+ "
\n",
+ "\n",
+ " a) Jacobi: O(N^2) = O(R) per iteration, GE: O(N^3) = O(R*sqrt(R))\n",
+ " b) Jacobi: O(N^2) = O(R) per iteration, GE: O(R^3)\n",
+ " c) Jacobi: O(R^2) per iteration, GE: O(R^3)\n",
+ " d) Jacobi: O(N^2) = O(R) per iteration, GE: O(R) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0244f525",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "answer=\"x\" # Replace x with a,b,c or d\n",
+ "pdes_check_1(answer)"
]
},
{
@@ -462,22 +560,7 @@
"id": "638b0ecc",
"metadata": {},
"source": [
- "### Algorithmically scalable solver\n",
- "\n",
- "- If the total cost scales at most linearly with respect to the total problem size\n",
- "\n",
- "
\n",
- "Question: Are Gaussian elimination and Jacobi algorithmically scalable?\n",
- "
\n",
- "\n",
- "- Answer: NO.\n",
- "\n",
- "- Problem size is R = O(N^2)\n",
- "\n",
- "- Gaussian Elimination: O(R^3) (more than linear)\n",
- "\n",
- "- Jacobi: O(N^2) = O(R) per iteration. Linear per iteration, but how many iterations?\n",
- "\n"
+ "To conclude, we find that none of the two solvers are algorithmically scalable. The Jacobi method scales linearly with respect to the problem size per iteration. However, the number of iterations might increase with the problem size, as we will demonstrate in the next section."
]
},
{
@@ -485,7 +568,9 @@
"id": "4ca3d718",
"metadata": {},
"source": [
- "### Complexity of Jacobi method\n"
+ "### Complexity of Jacobi method\n",
+ "\n",
+ "Next we implement the Jacobi method to solve the PDE. In addition to using a fixed maximum number of iterations, we introduce a relative tolerance threshold. The iterations stop when the relative error in an iteration subceeds this threshold."
]
},
{
@@ -518,6 +603,14 @@
"end"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "a3b1b69d",
+ "metadata": {},
+ "source": [
+ "Run the following code cell several times with different values for $N$. For instance, $N=10$, $N=20$, $N=40$. Compare the number of iterations that are needed to converge for each problem size."
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -525,7 +618,7 @@
"metadata": {},
"outputs": [],
"source": [
- "N = 40\n",
+ "N = 10\n",
"u = zeros(N,N)\n",
"fill_boundary!(u)\n",
"u, iter = jacobi!(u,reltol=1.0e-5,maxiters=1000000)\n",
@@ -538,7 +631,8 @@
"id": "791691b8",
"metadata": {},
"source": [
- "### Convergence analysis"
+ "### Convergence analysis\n",
+ "We can observe that the number of iterations scales with the problem size. In the next step, we plot the number of iterations needed for convergence against the problem size. "
]
},
{
@@ -576,9 +670,7 @@
"id": "4886157c",
"metadata": {},
"source": [
- "- The number of iterations to achieve a relative error of $10^{-s}$ increases as $O(s N^2)$\n",
- "- Remember: The cost per iteration is $O(N^2)$\n",
- "- Total cost is $O(N^4) = O((N^2)^2) $"
+ "It can be analytically proven that the number of iterations to achieve a relative error of $10^{-s}$ increases as $O(s N^2)$. Recall: The cost per iteration of the Jacobi method is $O(N^2) = O(R)$. Treating $s$ as a constant, the total cost of the Jacobi method will be $O((N^2)^2) = O(R^2) = O(N^4)$.\n"
]
},
{
@@ -588,7 +680,7 @@
"source": [
"### Complexity of some solvers\n",
"\n",
- "Work to solve a Laplace equation on a regular mesh of $S$ points ($S=N^d$)"
+ "There exist several methods to solve a Laplace equation on a regular mesh of $S$ points ($S=N^d$). The following table compares the scalability of these solvers for different numbers of mesh dimensions."
]
},
{
@@ -608,25 +700,27 @@
"id": "d24408b4",
"metadata": {},
"source": [
- "### Conjugate gradient method\n",
+ "## Conjugate gradient method\n",
"\n",
- "- Idea: Transform the problem into an optimization problem\n",
+ "In this section, we will discuss the [Conjugate Gradient Method](https://en.wikipedia.org/wiki/Conjugate_gradient_method). Combined with the multi grid method, it achieves the best scalability for solving Laplace equations.\n",
"\n",
- "$A x = b$\n",
"\n",
- "equivalent to \n",
+ "The idea of the conjugate gradient method is to transform the problem into an optimization problem of the form\n",
"\n",
- "$ x = \\text{arg }\\min_{y} f(y)$ with $ f(y)= \\frac{1}{2}( y^\\mathrm{T}Ay - y^\\mathrm{T} b )$\n",
+ "$$ x = \\text{arg }\\min_{y} f(y)$$ \n",
"\n",
- "- We can use some sort of gradient descent to solve it\n",
+ "
with
\n",
+ "\n",
+ "$$ f(y)= \\frac{1}{2}( y^\\mathrm{T}Ay - y^\\mathrm{T} b ).$$\n",
+ "\n",
+ "Thus, the goal is to find vector $x$, the minimum of function $f(y)$. This vector is equivalent to the solution of the system of linear equations, $A x = b$. \n",
+ "\n",
+ "The conjugate gradient method is a gradient descent algorithm optimized for symmetic ($A^\\mathrm{T}=A$) positive-definite ($y^\\mathrm{T}Ay > 0$) matrices. The algorithm is applicable to our problem since the matrix $A$ is both symmetric and positive-definite.\n",
"\n",
- "- The [Conjugate Gradient Method](https://en.wikipedia.org/wiki/Conjugate_gradient_method) is a gradient descent algorithm optimized for symmetic ($A^\\mathrm{T}=A$) positive-definite ($y^\\mathrm{T}Ay > 0$) matrices\n",
- "- It is applicable to our problem\n",
- "- It is a type of Krylov subspace method\n",
"\n",
"### Top 10 algorithms of the 20th century\n",
"\n",
- "According to IEEE Computer Society\n",
+ "The conjugate gradient method is a type of Krylov subspace method, which is listed by the IEEE Computer Society as one of the top 10 algorithms of the past century:\n",
"\n",
"- Metropolis Algorithm for Monte Carlo\n",
"- Simplex Method for Linear Programming\n",
@@ -641,10 +735,20 @@
"\n"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "a9f516e6",
+ "metadata": {},
+ "source": [
+ "### Convergence analysis\n",
+ "\n",
+ "For the following convergence analysis, we will use the implementation of the conjugate gradient method from the `IterativeSolvers` package."
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
- "id": "cb4068bb",
+ "id": "9f8dbf39",
"metadata": {},
"outputs": [],
"source": [
@@ -660,24 +764,6 @@
"visualize(u)"
]
},
- {
- "cell_type": "markdown",
- "id": "a9f516e6",
- "metadata": {},
- "source": [
- "### Convergence analysis"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "100f1b77",
- "metadata": {},
- "outputs": [],
- "source": [
- "#plt = plot(xlabel=\"N^2\",ylabel=\"Iterations\");"
- ]
- },
{
"cell_type": "code",
"execution_count": null,
@@ -700,6 +786,14 @@
"plt"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "5396554c",
+ "metadata": {},
+ "source": [
+ "The number of iterations needed to converge is much lower with the conjugate gradient method than with the Jacobi method. However, the number of iterations still increases with the problem size, so there is still room for improvement. "
+ ]
+ },
{
"cell_type": "markdown",
"id": "92bfd7d7",
@@ -707,11 +801,10 @@
"source": [
"### Number of iterations\n",
"\n",
+ "It can be shown that the number of iterations to achieve a relative error of $10^{-s}$ increases as $O(s \\sqrt{\\kappa(A)})$. As a reminder, the condition number of A $\\kappa(A)$ is ratio between the largest and smallest eigenvalues of A: \n",
+ "$$\\kappa(A)=\\dfrac{\\lambda_{max}(A)}{\\lambda_{min}(A)}$$\n",
"\n",
- "- The number of iterations to achieve a relative error of $10^{-s}$ increases as $O(s \\sqrt{\\kappa(A)})$\n",
- "- $\\kappa(A)=\\dfrac{\\lambda_{max}(A)}{\\lambda_{min}(A)}$ is the condition number of A (ratio between the largest and smallest eigenvalues)\n",
- "- In our example, $\\kappa(A) = O(N^2)$\n",
- "- Thus, the iterations number scales as $O(s N)$\n"
+ "It can also be shown that in our example $\\kappa(A) = O(N^2)$. Thus, the number of iterations scales as $O(s N)$ for the conjugate gradient method. Remember that the number of iterations scales as $O(sN^2)$ for the Jacobi method.\n"
]
},
{
@@ -721,7 +814,7 @@
"source": [
"### Goal\n",
"\n",
- "- Find an iterative method whose number of iterations is independent of problem size"
+ "The conjugate gradient method provides some improvement to the Jacobi method, since the number of iterations increases more slowly with the problem size. However, the ultimate goal is to find an iterative method whose number of iterations is _independent_ of the problem size."
]
},
{
@@ -731,31 +824,25 @@
"source": [
"### Preconditioner\n",
"\n",
- "A linear function $M$ such that\n",
+ "To achieve this goal, we will apply a [preconditioner](https://en.wikipedia.org/wiki/Preconditioner) to transform the original problem into a format that is easier to solve. \n",
"\n",
- "$M(b) \\approx x$ with $Ax=b$ for any $b$\n",
+ "A preconditioner is simply a linear function $M$ such that \n",
+ "$$M(b) \\approx x, \\text{ with } Ax=b \\text{ for any } b.$$ \n",
"\n",
- "$\\downarrow$\n",
+ "Once we have found such a function $M(b)$, we can solve the _preconditioned problem_ $(MA)x = Mb$, which is equivalent to $Ax=b$.\n",
"\n",
- "$ M(b) \\approx A^{-1}b $\n",
+ "The way that preconditioning affects the number of iterations is that it reduces the condition number of $A$: \n",
"\n",
- "$\\downarrow$\n",
+ "$$ \\begin{align}\n",
+ "&M(b) \\approx x, \\text{ with } Ax=b \\text{ for any } b \\\\\n",
+ "&\\rightarrow M(b) \\approx A^{-1}b \\\\\n",
+ "&\\rightarrow M \\approx A^{-1} \\\\\n",
+ "&\\rightarrow M A \\approx I \\text{ (Identity matrix)} \\\\\n",
+ "&\\rightarrow \\kappa (MA) \\approx 1 \\\\\n",
+ "\\end{align}$$\n",
"\n",
- "$ M \\approx A^{-1}$\n",
+ "Recall that the number of iterations to achieve a relative error of $10^{-s}$ is $O(s\\sqrt{\\kappa (A)})$. With $\\kappa (MA) \\approx 1$, the number of iterations will scale as $O(s)$. Thus, the number of iterations will be independent of the problem size for the preconditioned problem. \n",
"\n",
- "$\\downarrow$\n",
- "\n",
- "$ M A \\approx I$ (Identity matrix)\n",
- "\n",
- "$\\downarrow$\n",
- "\n",
- "$\\kappa (MA) \\approx 1$\n",
- "\n",
- "$\\downarrow$\n",
- "\n",
- "Conjugate gradients will be fast when solving\n",
- "\n",
- "$(MA)x = Mb \\Longleftrightarrow Ax=b$\n",
"\n"
]
},
@@ -766,9 +853,12 @@
"source": [
"### How to build a preconditioner ?\n",
"\n",
- "- $M=A^{-1}$ (exact preconditioner, but as costly as solving the original problem)\n",
- "- $M=I$ (no extra work, but slow convergence)\n",
- "- We need a trade-off (maths + computer science problem)\n"
+ "Many ways of building a preconditioner can be found in the literature. The solutions range between two extremes: \n",
+ "\n",
+ "1. $M=A^{-1}$. This is the exact preconditioner. The CG method will converge in just one iteration. However, it is exactly as costly to compute the matrix $M$ as it is to find the solution to the original problem. \n",
+ "2. $M=I$. This is the other extreme where we do no extra work to find the matrix $M$. The preconditioned problem is identical to the original problem.\n",
+ "\n",
+ "The solution is to find a good trade-off such that we get a matrix $M$ that is accurate enough to approximate the original problem but also cheap enough to be computed quickly. "
]
},
{
@@ -776,7 +866,9 @@
"id": "4338ee30",
"metadata": {},
"source": [
- "### Jacobi Preconditioner"
+ "### Jacobi Preconditioner\n",
+ "\n",
+ "Next we use the Jacobi method to construct a preconditioner for the CG method. The matrix $M$ is simply computed as the grid values after a certain number of iterations with the Jacobi method. In order for $M$ to be linear, we set a fixed maximum number of iterations in the `jacobi!` function and disable the relative error stopping criterion."
]
},
{
@@ -805,6 +897,14 @@
"end"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "46b3883d",
+ "metadata": {},
+ "source": [
+ "Run the following cell for different values of `niters`. The larger the value of `niters`, the more matrix $M$ resembles the exact solution. The fewer iterations we use, the less accurate the approximation."
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -816,8 +916,10 @@
"u = zeros(N,N)\n",
"fill_boundary!(u)\n",
"A,b = generate_system_sparse(u)\n",
- "M = jacobi_prec(N,niters=10)\n",
+ "# Generate preconditioner\n",
+ "M = jacobi_prec(N,niters=1000)\n",
"x = zeros(size(b))\n",
+ "# Compute solution as x=b/M\n",
"ldiv!(x,M,b)\n",
"u[2:end-1,2:end-1] = x\n",
"visualize(u)"
@@ -839,6 +941,7 @@
" A,b = generate_system_sparse(u)\n",
" M = jacobi_prec(N,niters=100)\n",
" x = zeros(length(b))\n",
+ " # Provide preconditioner to conjugate gradient method\n",
" _,ch = cg!(x, A, b, Pl=M, reltol=reltol,log=true)\n",
" iters[i] = ch.iters\n",
"end\n",
@@ -846,12 +949,22 @@
"plt"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "26f7e342",
+ "metadata": {},
+ "source": [
+ "Observe that the Jacobi preconditioner achieves that the CG method needs fewer iterations. However, the number of iterations still increases with the problem size. "
+ ]
+ },
{
"cell_type": "markdown",
"id": "43fb5082",
"metadata": {},
"source": [
- "### How can we improve the Jacobi method?"
+ "### How can we improve the Jacobi method?\n",
+ "\n",
+ "We can optimize the Jacobi method to obtain a better preconditioner for the CG method. Run the following code cell again with a grid size of $N=10$ and $N=100$. "
]
},
{
@@ -861,7 +974,7 @@
"metadata": {},
"outputs": [],
"source": [
- "N = 10\n",
+ "N = 100\n",
"u = zeros(N,N)\n",
"fill_boundary!(u)\n",
"A,b = generate_system_sparse(u)\n",
@@ -872,12 +985,24 @@
"visualize(u)"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "bf76a64b",
+ "metadata": {},
+ "source": [
+ "The main reason that the preconditioner performs badly for larger problem sizes is that it takes more time to update the interior values of the grid. The larger the grid, the longer the Jacobi method needs to propagate the values from the outer boundary to the interior of the grid. "
+ ]
+ },
{
"cell_type": "markdown",
"id": "c04df158",
"metadata": {},
"source": [
- "### Multi-grid method\n"
+ "### Multi-grid method\n",
+ "\n",
+ "The [multi-grid method](https://en.wikipedia.org/wiki/Multigrid_method) is the second building block of the fast solver for our Laplace equation. \n",
+ "\n",
+ "The multi-grid method achieves a faster convergence of the Jacobi method by starting to solve the coarse problem first and then stepwise increasing the grid resolution. On each resolution level, a few steps of the Jacobi method are performed (only 2 steps per level in this example). Next, the resolution of the grid is increased and the Jacobi method is run again. This process is repeated until we reach the desired grid size. Since the Jacobi method converges after a few iterations on very small grids, the values are propagated to the center much more quickly than without using the multi-grid method."
]
},
{
@@ -933,7 +1058,9 @@
"id": "6122ac8b",
"metadata": {},
"source": [
- "### Multi-grid preconditioner"
+ "### Multi-grid preconditioner\n",
+ "\n",
+ "Let's see what happens when we use the CG method in combination with a multi-grid preconditioner. In the following code cell, we use a multi-grid preconditioner from the package `Preconditioners`."
]
},
{
@@ -962,14 +1089,10 @@
},
{
"cell_type": "markdown",
- "id": "ab13519f",
+ "id": "63c406e0",
"metadata": {},
"source": [
- "### Number of iterations\n",
- "\n",
- "\n",
- "- The number of iterations to achieve a relative error of $10^{-s}$ increases as $O(s)$\n",
- "- The cost of each iteration is proportional to the number of grid points\n"
+ "The number of iterations is now constant for different problem sizes. Thus, we have finally achieved an algorithmically scalable solver by combining the conjugate gradient method and the multi-grid method!"
]
},
{
@@ -979,7 +1102,7 @@
"source": [
"### High-performance conjugate gradient (HPCG) benchmark\n",
"\n",
- "- Alternative to HPL benchmark to rank the [top 500](https://www.top500.org/) computers\n"
+ "The HPCG benchmark is an alternative to the HPL benchmark to rank the [top 500](https://www.top500.org/) computers. It is based on the CG + multigrid method, whereas the HPL benchmark is a Gaussian elimination type of algorithm. The HPCG was introduced because the CG + multigrid method is more commonly run on supercomputers than Gaussian elimination. While HPL gives a good indication of the peak performance, the HPCG benchmark is orders of magnitude below the peak performance. \n"
]
},
{
@@ -1003,7 +1126,8 @@
"id": "b30f96ad",
"metadata": {},
"source": [
- "## Parallel implementation"
+ "## Parallel implementation\n",
+ "In this section, we will discuss the parallel implementation of the CG method. "
]
},
{
@@ -1011,7 +1135,8 @@
"id": "b5eadafc",
"metadata": {},
"source": [
- "### Conjugate gradient method"
+ "### Implementation of conjugate gradient method\n",
+ "First, let's analyze how to parallelize the individual parts of the serial implementation of the CG method."
]
},
{
@@ -1051,11 +1176,13 @@
"id": "e0bb3512",
"metadata": {},
"source": [
- "The phases that are not trivially parallel are\n",
+ "The algorithm performs matrix multiplications and additions which are trivially parallelizable. The phases that are not trivially parallelizable are\n",
"\n",
- "- Dot products\n",
- "- Sparse matrix-vector products\n",
- "- Preconditioners"
+ " - Dot products\n",
+ " - Sparse matrix-vector products\n",
+ " - Preconditioners\n",
+ " \n",
+ "If we find parallel implementations for these parts, we get a complete parallel implementation for the CG method. "
]
},
{
@@ -1065,8 +1192,10 @@
"source": [
"### Dot product\n",
"\n",
+ "The dot product is defined as \n",
+ "$$\\text{dot}(a,b)= \\sum_i a_i b_i$$. \n",
"\n",
- "$\\text{dot}(a,b)= \\sum_i a_i b_i$\n"
+ "Imagine we have our data distributed on two CPUs as in the picture below. How can we implement the dot product computation in parallel? "
]
},
{
@@ -1080,31 +1209,58 @@
"metadata": {},
"source": [
"
\n",
- "\n",
+ "\n",
"
"
]
},
{
"cell_type": "markdown",
- "id": "a8aa2ebd",
+ "id": "93ec2564",
"metadata": {},
"source": [
- "### MPI implementation"
+ "### MPI implementation\n",
+ "For the parallel implementation, the processes can compute the dot product of their respective sub-vectors independently. But they have to send the result to the other processes so they can be combined for the final dot product. Each process needs the _global_ dot product, because further computations in the algorithm depend on it. For instance, the stopping criterion depends on the result of the global dot product, such that all processes stop at the same time. "
]
},
{
"attachments": {
- "g2419.png": {
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MrBnHjdQwhAkunPXS5bcJ8H3gSuAl4AvgRdJf7k6KLrQKNdG9QXe2wvNLYFGeo2fiOIxGd1xyv/mOoA/wXeAPWOK3EEuOUu+ZMItESnwcDYwEDgm53TWA3yav/wuIYueQjbBz6T+AGdhsnpdI/585NYKYSmEIcAowHngFOz/OIP17jmxDm79L/rtrGx8vIiIxU5f1ntN9HxLsm0z0+uGpxbEAz6vU8hjj3GetPm6034UE3Vf5eS1NwHwSvEWja30E+EzflxVskzz+1azPkdLgV8ezQ/L4Nxjn5uQ8Ph97/s2B1QDowpNc4Za3q81KNsG9DRzFaX4tajgU2AIYhMdTwxwSvIzjHsa7+RFH2l6voinWxbvMLQUuYqT/C33YC9gbG+XuDXwKvIfjfsa7VyKMslQuAeqjDkI6nFuxzvOPIdQtGMdiifxy4IIQ2y3GdDr+dPBa4H8laPcR4DFgL+zccxewrATPIyIiHcSqSfwZfh2aGEsz38+43wX+TdBEvb+XGn63yhd4z79wbLZKu+mx3qWM9pNp5jer7DO+gh1x3Jds5yhslDiXzXA8nGy/HpiY5/hMZ/nOrOAnJNgex/asZL2MiWzLGQjMLarNajTRfUqxf7vqlcC+hK0BDIw4luowya3ARqMfijqUCH2KnQu2jjoQkRY2AMYkr18DzI4ulG954A2gFzA44lhKqRmYiS3Jau+yrAuxJH494Azgr+1sT0REOrDM6fSj/R408TIwCkvgv8AxBcflwJ+Aa7F1snU4DsczgzF+vSxtLwPeD1wWJH/eFc8J1PAyo/32of9GxVhOTzwX4zga++CUjulh4PfAgVjyvgUwLdKIpNK9i62hPQmbJrw29h4SqTSjSc0es8/oqEzFlp98FzvPDgOeiDCeUvDA/dj09/2x33MrbNlAez1CemeQM9FyRxERySE90j7ab4XnAaArloD/ks5c2ep08tP8dtTwR+AgmunUasueaUxwe2f87Ay/Nk38Cvgh0AvPDYz1mzPWNYX1C7XBx3hm4JIXz17ALyKMR8J3aNQBSNW5NOoARArQCVufDdbxFGXn5JERPne5JLBOilK1fRNwDja7Yn/ggRI9l4iIVDlL4hv8anhuwxL4lSQ4mIluatZHTXQzgO/S4M/EF1Hh/Eo3DziL0X4QnhHApnzC7sCj7fgd2q7RzQfWyfhZg98yklikpTqsyM++2IhOP2xkYgHwFlbF90lsZESkJQcchBUS2wBYis0iuhl4NsK4pLQ2wGZPbIfVbpiHjZzegr0HirUjMAI7B62BFWubhZ1/7id3UdD9gPVb/Cy1YKs70JDlcc8CrxUY39FA/+T16yn+fFgL7AL8P9Ln2TpgPvb/5X5srXZHOs/2BU4Gdk9en499ltwI5K7DU3o3YEk82BIJJfEi5bMa0CN5fTnQUQr1djS9sM8usELVHenzqSiWxDu+h2cTADyX5Ezggxpd27ZsSXA7jhHJ59uOqJJ4qVQXYVNE++Q45lxsm6jTsLWXIindgCnA4S1+fiBwFradUwP2IS0dx9nYuuIuLX4+EvgpcFgRbfUB/s2q76GUM7AdLk4ie6Gz0cBROdofn+W+syk8iT8mcL3YhG8sNiuub45jfgE8j51nC42pku0K3I4tjwkaAfwa+z2nlDuogNewKfWDgYOxhGJxhPGIxMm+WMclQCN2Du9IHsRm+LwIbE/1Jr/Pkt7mtyfhnCMdtixqK+Bewt9BpiQsifffbv2yPLn+vbRqmP3tW8fRq+TPJ9Vmd9IJ/ApsG595ydvrYvt1d8G+kD0G7Ilt/yUCVhBqP6zo1DSsKN3GWC0EsO0Gu5KZAEl1+yGZhcA+Al7GzhO7A5sDk8i1I0tad2x98jaBn72E1XbpD+yMjdhsi51/9qD188+nyccEbZD8tykZY2u+LCBGsJlJuyevL8N+32LsRjqBX4nt4vEJ9sUudZ7tis1GeBQruvZ6kc9RSfpjCXp/bPR9GvbFbVfs82YNbPeAQ7Evu1GZhs2wqAOGRxyLiJRPX9L1ueZi5+OwHIIl8AC/oXoT+FLxWE2X27EO1P0gWTi9gtVwiu8C7ASA59nkFPPSSnz7RQby70Et8eOBO7BRsF7Yl8jDkpetsW3Obk0e25/sI1oST/th64O3wBKVo4AtsQ+x1PS4kSiJ7yg2xAqvppyHTWM/HDgAS0gfwabY5xp1TrmAdAI/F0vytsUSqz2Sz5dae74mti97a0XIzkweG7ykpt/Pa+W+1OXfBcQI9p5eI3n9JazDs1j3YKPQq2MjM4dhf7dtsN07bkge1xe4ug3tV5KtsM+Lq7CR7kOxc8IgrKo/WOfMBGhlm9zyeS5wfY/IohAJx43Aj8g9s1LMd7GtOqdjs73CUodtXQnwAunZBpJpCvb3AfgbhXX6R6qGOjYhvYf2iyV/xrG+hhpO+va2z/jAEgFLrkZge+W2NuV5ITaNdVby9h7Ad8oSmVSDBJa4t1xmcS/ws8Dt88sWkZTST0lPob8O+7ISXKs+H3s/zCO/vqS3awM4llVrKMzGEsDUjiu7YNMwy2144HpbPrtPwn6PO2i9XsAXWNG8t5O3d8RG56vZk9isjeDnylKgHpiRvL0OcGKZ4wqaEbi+W2RRiIRjFtbJ+gbwONZJuFquB0joRpOefv5bNAqfjceW84L9vU6OMJaC1FD3bU8+2Jed0jjLd2aM34Y53Ipnn+RPX2KCikzJKuYWcMxK4O7A7S2yHSixcy82Nbg1/8I6gcBmdWxWloikVGrInFGRbVeBL7EtA/M5knSn9mNY0tea+diIbsoJBbQdtuD+65+24fGFnGebsc7UlGo/z7bs4ElpInM5xnHlCadVwc6mwVmPEqkOF2AzbtfCBlyuB+ZgOzFEu810PDhsJgTY373ip4hH7G7Sn6c/JV2MtiLV4On27S3HstBaduxGg1/47WU5S0jwYnJPdoC51PI9cOoRkraaE7jeslCRxFeuAl8ryCykuWOJY5HS2girqA5WEGxmjmMLmUK4c+D6fXmODd6/UwFth23NwPWFWY9qv48D16v5PLsS28s+m4cC13cgun3aFwSur5n1KJHqsASbHp7SE3tffw87J8/C1iKrw6o09oVk4XJbHtUcYSzVoAmrnwM2Gj88x7GRqyPBF4F+hjCLzNXR+hqYj3DchucSrirD+nupVqmCQyNIr4PvQeZaxWAV6q7lC00qXK5EDjKLkA3NepRUgyGB6/l2qXgDmy6Xq2c92F6+Im7B99km2GdeU57HhCmsJH4X4Ahs3f8g7Et28DzbOXC9ms+z75N7m8H5wOfY37U7VkthVunDWsVC0u/TntjnXHgDLJnWhW9nRoqUSmobsJbn3n7Jyy+BH2MDM38GbqN0uzJsin2v3DB5O7X17Ow2tOWwDr/9sU6Ibth5ZDrWQZGr5lctsHeLnw0LXF8f2/qzNY9Dwdt7Byvs35D1qOxqsBkTu2Kd5n2wjpnPsPodU6mMHTS2wV7X7wBfY5/3/6Fts9RuwJZdgS2veyqMAEuhjjrmBfpl1g2tZccMmgNvHsfXJJjP1S77l40afGClRv5ecEdNoMq9RvQ7jnWwta17FfEYrbGSlHydg8H7Vy9lIFJyvQPXF2Q9yiwBviG9D3C+9j7P095XWHLVBfu8Wr2AGMIULLrTltGVgcC1ZP+i2JpqPs8WMmgwn3TnSO9cB5ZQgsyEp5TFlban8EKKIqVSiw0i9gKuxJYqnYQl82HpjBUrG82qnQkXYNsWZ9sutDXDsG3ods1y/9fAH7FOidbyk67kntp+fPLSmjUorCh4D9LbpL5PcbuL9MT+XgdjSyGy+RL7PS+j9aVKpdYFWyr3/Vbu+xPWQfS3Itt8Dkv+18Lq6dSTuwM4MnVcxQc0fBvszvkeULAEXzPRzch/YIAP9OYkcn7RSh3fM+P5pCPojfUyrp+8/RW2JvMdrJf2q8Cxh5D+j1vR61akrPJV6Q6OalXzyKJkzsZprQhmS8vIncQH3w+FtpeKoRvlTeKDnQxrZD2qdT2xNf8bJ28vxtYCvoWdZ4Pb3B0A325DW83n2UJfz5Sozg29SQ9iLKO0o1zzUKVqKb1BWD2NXOePBPb97lUs8Q3zfVkD3ILNOEr5DFsq9B2soOlfsEr6hdgKG4EOnnffxTqJ18c6I3piNVrWJ9xK88UYTrrjtdgi4qsDowK3PTZbYT52jhoMdEoe92esvlAD5S+adxnpQqQfYrM+Uq9B1+T9XUkXrCvUc9huLV2w3PjR3IdHo87WpPuHsF6voYz22zPeTc/3wJJwfBoYWd8g57GpY1LH1xRUeVgq3/mkE/gnsG2dso2IbZLl5xJvXfLc3y1wXZ1/1S2Y4OR73SHztW/NN4HrhSRxUb6XgiPLxSbxvyCdwD+LTUPMNu2wo+z8Uez7I6pzQ3AbxHyzQdrrGWxbK5FSmohtidmSx0aU5wP/wIrefVGC5z+VdAL/FZacTsE6DmqxwaArKaxAaSdsunXqnDsN+AHpZXpdsCnYf8Jm0ZyOJfz/adHOYlbt1DgJm4UKNjvg1wXEk8tegett2QmsGfs73YQlscHXphYrBHsVds46Dfs9b25LoO1wIpa8H4+dz8Beg59jtRYcNlPgPuCVItqdhiXxYAUZKzKJT/X2pqcaeC5lrC+soMtI34mzfXi91eN5l9RIhi9gnVbi2219mlhCcaP+UqlShQ+bsf+cub7EDCh9OFKF8r0vgsW5VJejugVfv3yve2/yJ/HBkfSBeY5dE/tCB7Y+8ascx5ZCsOO6b9ajWpc6z3rsC2yudYMd5Tyb7/WEzHNDqRPobIK1DjQ4IdXOseqSnYXYjJ+/kt668gpKk8DXAmMDtxuA20lP/W7GlpT8jMIcB2yevD4bOIjMOjvLsJzql4Gf/V9REYdn98D1F7Ie1bpFWM2gkVgi3/K1acYKwJ0a+Fl9sQGGoAlLtp8J/GwZ9jdP7UhTi22tV4zg32uPNkdXYpasN7oXcVyT/NnefMI/GOlrcz5yjF+PPjzGYtYJLxznSa2BcexKvc++Vu80vyUuudbDcTfXu2+yHivVohZYL3l9FvkLjYS3/EM6kq3y3B/cJivbVnRSHV4LXB9K7loq+d4XLdvbOs+xwfZmUth6wNTa9TDWlk/LEks+Dr6d6fYJNg00l45ynl2X3Ovc1yVdI+Mz2lYQKQzB911bRs9EKsmhWCfjYuz/1BRsVHwdbLT0vRI//96kO/DeIPs6+0Yyd+LI5sTA9T+RvePhctLnkE2J5jy6XuB6IVuKBn0DvF3AcfeQXqq0bZHPEYbbyf497gLSn7kHU1wNpODuV+sVH1Z5pAumLOGHdGMLPNsBp9OHnRntL2E5D3KNszfpSF9LX3YmwXEkqCezam04mrnEtp6jN47JNPhf0JmbucLZKEeD74ZnBI6/YF+EltGc0ctWnAa/G5kjDekv+I5DafDp4hF1TONK15YKllKYGtLTi/JND90a2K604UiVOhz7cG1NL9K9qs1k9t5K9fkE+xK4IVbleFeyV5I9PMvPg54GzkxeHwH8nuxr/I5s8bhCfI2d29bEPr8KrTDcmhewYjtdSW+JVmhhoVQnfWr9dbbHDcWq10ftFDLjGA+8WGQbNVgdlWwVmg8NXH+iyLbDFPyyX2gcfSis0JVIuV2EJc9/AyZTup0WsgluEXYf2c/nTcn7G3K0VUPmeejOHMeuwNb1n5K8vSuZHa/lUI5tSJuxz+ChWJLcg/JWq787x33zgBnYbI/OWM6Qa5vRoKrY6jOdxF/vvuEMvxcr+XdyL/dt8NxCJ6DBf4l9WehPImOk4xnqQp5ydrX7kHo/Esd/sDfEOJbzTxr8p9gXjbVw345iLAVOZqJrz2jaeVgPzao84zNur+RY2rYNhRRmJfb3XRf7UrI78GQrx3UDxpUxLqkuw4EDaX2/+HNIT6l+iOhG2yQ8N5KeKjcW2+6nZVL6HQqb6ncnNi2+F7aG83haL3a0KbYOMuW6Vo5pzdtYkpZKKKcU+LjWLMe+FO6NxTuE/Nsrgn2JfR+rKdId22Lsv60c1wVLliuhmN2epL8Mg/3fLTaJB5viOhnbqSCoF5nTaa9vsyV38gAAIABJREFUQ9th2Sn5r6ewJH5trENnMvBTtA+0VJataV9nZXttHrierzr7a3nuX590YdRFwEd5jg/mJltkPao0Vie93KuJ9i332hlbEjEUS2h7krlrRrBuSlfKm8Tne81mYkk82Huh0CS+5VafXanACvWZW5dc6RYDIznN704NP8Y+3Ptgb4bUNISv8UwFJjKBe5NT4NM8s3F0ovipG2kT3H8Z47cmwXnYaMeaZK5n+wK4mwQXMdHl2xs4N888HO8XdGxNReyF2NHdDpydvH49VoAk+J9uZ2ya0o5YT1kha0EPYNWpscGieHuTHplKmYxVxJfqdBPWoz4F+1LbDduH9vzk/QngdzkeP4jMaXOQuYdrf2xLmqC3gDvaGK+03eVY9d9+wL7Ytmk/w6ZEg50zriV3VfqUJdhaukuTtxuxRPdaLGl2wH7ABNKF0u4Eni8w1vtIj7Q2Yl9+UtvZpCwgszp8LneR3mt4HwpL4sHOs6n/C9dg59mHAvfvgI2c7Urh59l9sS3LgoYGru/Oqv9n7iRzPWmpDcH+Zg3w7ef+xsC/SBdUfR6bIprNIWSeC4CMQrz7QWDnHHMrhe05vy7pgoPPU1gn4+HY+epH2IyUEyj8/SNSalEm8JA5qzPf/6d8NSj6FHEsZOZBxRYfba9gIc8VtK1q/PbY59Q2RTymU/5DQlXMa1rMa9CcvKTy5C5UfBKfMtE9CTzJSF9LHwbhWQtPLTV8ziJmMcll7+md4PYPJbJxbhYwBvzpnMq6rMZaNFFDDZ8zkA8Y68LZj3CCOy2UdiQsF2H7Mq6LfcF9BOsR+xjbBjG1X+V0LEG7oIA2R2B7g2bzXVat0PsmSuKr1QRs1HUS1uH3Obb+LvihNpbchV7WAy7Ocf/ardz/H5TER2EhNio+GZuifiJWfOgjLAFPnTPuwJLS/nnauwzYDSuW0w0bjb4MWyPXl8wvAm9h1YcLdQVwMunp/5e1cszZFL6v7bXAhdgowYnJ9gvxJ+BYLPkcBDyIjSzNxv4+qQJvr2DTz7MtTwk6FOsoy2b/5CVoFoUl8S2/q7RlSu7T2JfwfbE6ALOxTpnBpGcbzMdG/HN9vzgGqyKdzRFkbmUFNmtgVgExHh+IpdDZZuOx89y/sFmFL2CJffsGOEQ6hmAx03znjXxJWvcijm15TCGdyGEKjiR3w6aTF7LNZspO2Pfv1O/8FTYz6CMsMQ5u5XsmfFsfrdwzt/K9DsH7u2c9alXB2QZNlKboYru1nsSnWLL+EfmnjJSQ81zNh9gWAlIqDX5DHJvjGQwk8MwhwStc7cr9d/8cG1G6ifQUmDXI/OJ8K5aUqwOmXEb5ntSxCzAAT28cn1PDuyxgRs5OvWhcio3Cno+t+Q0Ws1qS/PnfI4hLSuceLOmeiCWldWSOjt6OJfqFdMw1YdXbf4dNT+6KfQnaOHCMx/Yd/gnpEf9CfIGNxP8O66xsb+X3Rdj58BTSVZ7fKvBxqfPsrsmf9SFzpGkydo49vp0xhiE4wv8BmbMGCrUEG6X+T7K9dVvc/zKWnEeZ/KZm/yzEXtdC3Up6JtDG2DKLE8m9XlQkDoJFr/PV8cq3DWVwGU4hW1YGj2m5hKfUVmKfN6lz+hoUN0M6NQsN7HP1p2TfdvN7EGaR86J0IfdSgeBrUEwB9ODss/m0bSZDyeVO4qVja/CrYdMKxwCbZ7xFHTbBvMG/hOOfLOSaMiZr72FfdHfH1kIOwHrTPgTuJV3NdCLpNaULyO7XpKfHFkpb+wCc5nemll/h2Y/UB2CqnzUB9OFz6v0trMaFXOmi/JudQHpf79nYaz4RS5S+g/VA/w+bvltIgZcZ2GhpMbRDRrQeADbCZtVsi31p+QQrLpTagnQn7MyW7wN5JfYe+iu2HCe1FvArbBT3IQobWW3NfOCs5KVl4gy5z2WtuZL0evGTKHxv4Q+x+hG7YfsJD8RGqj7E/mapysTXJ29D7v87f6T4zrFCOkAGApsFbv+Bwqfovk/6//FS7EvsTtiU9+HYazoPG2F6gsLWk/8cii6mW8iX521JT9P/N8VP3XwZ65yYhL2eU4BfAZcU2Y5IRxIs+JivQFm+WVrB0di1sh7V+jGlKiyXy2e0LYnfAqsJAzYbazS5ZycV8rcolf7k/hwJvqbFfLb2C1yv2NpJSuLjqsFvi/Xeb5TnyG3wTKQPP+E0f0y7axAULlXUJ1dhny8pbO3ffLQfeHEafGoa8Qn4nNOj1sRxFk2MYrT/OeNdVAUHW/twmgX8pY3tLYMCa2VIJVmGjSBPznJ/sTOLFgI3tyui3BbR/qriL2AJ2wisQ/ZSiiti9BTZK/qTbKuQ9hZQfAdEIfYJXH+H7NXlW7OSVf8fJ7DlAw+2MZ7PKG72RaF+kfz3awpbvtCa+Vin0z+xWRQXY9sPnkoFrucUKYOZ2MwqyKzR0ZrN89z/AekdQfpiA0y5EuNgMbv/5WkbMhPlMPKz6djsLLAOwkJrpgwJXH+Q3An8INLLr6IwjNwFC4OveaG/P2S+F2ZkPSpiufbUlY6q3h+CVX3Pl8AHbU4Nz9Lg985/qFS1Bt8Pe3+cSOHrm7rjuYp6r2nqIuX3c2y2SV+swFlHEvzM+S223KGjGYqttQcrrNiekZ8VWE2Q0VgnxvewegAtlw+IxMGzgesH5TiuJs/9YOeeYC2dQ7MdiCXhwfaey9M2ZFZ1DyMxfjxwfaesR60quOyga9ajTK76ILlshBU6TV0OaWM7LetZBfUlvSx3OcUl48G/V5RbjuakJD5u6v3WOG4hs9hHoVYH7qDet6zMKx2FLbG4DZvaWTzHWTT4c0KNSUTyeR+4Knn9Z6w6Rb+apUbiZ2Lnpo7od9j3sQ8Ir15HI1bE7zOsuvQ0MvegF4mDR0jPnNmK7Enf8aR3qMjlpsD1n5P9u/Sp2Cg1WF2xQhLB4KyhHWh/jtbWJD64lfYerLp7U8p3sG2622IoNlModTm2je0cS/YByZ+SntFwL8Xt2hE8Vz6e9aiIKYmPkwa/Go5bKa5CY0u9qOFa8JWwd7CEzXM+mSNfbXER9b7lln4iUlp/wEZfz6WwLeGqwerYyNckrIhgOLvSVJZa4GHstTuGtlXez+ZJYBdsuukA4DGswKNIJVoNG5F9gPCmMDeRuZPMtVitpaBDsdoihbge62wDKyI5hcxR8xpsFmNwh5ELKOzc9T+sjgtYknsTtj1yWyvbv026mOt2RbTzLOmlUVth27i2zBv2xpLb1Yl2qU5nbNvQYN2UGmxpWWpL0wSF7WSV0o/0dPo3aHv9m5JTEh8vp5G5P3rbeLZjNMe1PxypKGN8fxw/D6GlGpyKKYmU2SJs9LURK77XEXyJJbbHAP+NOJZSacaKcDZia1jD9j6WyE/GvvD+C6t3oppIUglqsYRwEjZifj2WZF+V60FF+gfWgQWWoD2Gzex5CEty78K2FLurgLaWYNuEpqrN74+NtD+LdcZ9hP0Oqarod2Db3haimcyimccCL2F1MnzgUsxMq8bkv12AIwt8zHLgl4HbZ2K/16PYjhfvAlOxkfirgNeKiCel5UBgWwtn342t4Z+JnT9Tr8FVpGcQXIht81moYwKPbcx1YNSUxMdLrr3Si+NDbEsqQ4ITsA+yMOzP6X6D/IeJiEiJLcaKe52HjUo1YNNLO9KyC6kuw7BEaw42mn006S1h3wWuDvG5VmJbkN4e+NlQbJeKjbDE+AIK7zh4MvnYN5O3V8OmX/8/0lPoVwKXYQlhMduTTQC+T3p3kPa6hvTsnmLWrzcC55DeCWQNbNeLQ7AdPzzwZ+DHbYxryxa3b2r1qPzGAPdh+ex2rPoa/Bb4TZFtprb6XILN3KhY6omNizF+PRJsFWKLu9Hg+9HoVPW9o/AcVnAZu0I0cxiZU8pERCQaHttubiZwIzaC+DxwBMVVbRZpq2HYWvGjsVkhrW3ptiR5TNj7cn+dfN5dsZ08UrNS38QSyFewZUj7JX8+J097z2CJ6AFY4vgdbLT7c2zU9w7aPg37+uRlELZOv+We9ItXeUR287FZDidhMx4Gkf93S/kTVofkeKyuRm+s6OZb2O5Wqan6ZwG9ktc/L7Dt4K4jT2KzIgp1Gunp/Z9iyyEOAA7H9qtfjM0OuIXiZ6VtRHo9/C20f/eYklISHxc+Y6uLMNTiGEoFV22UIrlVekbbK+z3nIiItM89wO5YkrERNg34+8nbImFbF5uOPRKrdL4W2Xe9SWDvz+dLGM8zyUtrFlDcsp2VWLz3tDeoLOZQeMKdy++xqfmdsN1Lzs19eIYPgYvyHFPs69WFzMJxvy3y8c+28rP7k5f2Oht7f64g/+8dOU2nj4tECfZxTDAw9DYlGmf5zoQ/tVLvDxGRyvMqVv16KraEajK2FlcFayVsO2CjwGtgBeByvcc+B84oR1Ax8x7ppQJnYh0pUdqN9OyCh0nXK4jaQGBU8vrlVEFtGSXxcVFTgqq+NR1yv954Wk6C8KevtbVQiYiIlNYCbArqJVhi9Tto8/azItncjiVHpwAfk/17wdfYe3FBlvulfX6P/W27Y2vdoxScSl/sKHwp/RrrXFhI5o4GFUtJfFwkvt22orLblGg0upXY2qkwhTENTERESqMJK3Y3Gps+egzwNLa+VyRMd2Dr0b/Jcv884O/lCyd2FmGjzJcAXxFt/tcP20JwAjAtwjiCarGZIJdgy4sWRhtOYbQmPi4cL2PrjcL6j7sCp2I4HcyLwIGhteZ4KbS2RESkVBqx4na3Y/tST8fWMD8WYUzSsWwI3IkVQPNkTqtfgCWYmr1XWndR2DZ6pdYQdQCtaMZmI1UVjcTHRaObiwu1WMhUGt2XIbYnUXPcGWJrnuaSFXsREZFwPQ1sjyXw/YAHsSrQIu21B1aMbBjwCVY8LDXSmQCeA56KJjSR6qUkPk48V4TWlguxLakMK7kR+CyUthyTmOg+DqUtEREph4+xyvU3YJWsJwDjsX2wRdqiAav4viY2229nrGhYauuu+aizSKRNlMTHyUBuwTGj3e04pjLe3RdCRFJJ/uW+Bi4IoaXlNPGrENoREZHyWoatCT0PGyVtAB6h9T29RbLpDFxNuhPoJqwq+ezk/ZOwLdr+CcyNIkCRaqckPk7GugSOo7HiDW01l2ZODiskqTCNXIHjtna28kOudhW/NYeIiLTKYwWeDgW+xEbnpwPbRhmUVI01seUYqXXu5wEnAEsDx0zDZv5VRRVwkUqkJD5uxrlZOA6nbYn8HDzf1TTpjsx5ajkVx4NteHACx3k0uomhhyUiIuV2H7AT8BawDvA4cGSkEUmlSxVG3BObMn8Q1iHU0iPYVmMryheaSMeiJD6OxrtnqWFHiikk4nkY2IEJ7uWSxSWV4Uq3mIUcjPWQF/oB+wmeEYx3rX1Yi4hIdXoL2xrsv0AP4D/YZ4O+P0pLx2EFEtcF3sbeNw9nOXZx8hgRaSOdhONqnJtFo9sdxxHJUdfWkrVlOO7BcRAT3P40Oq1biotJrplGdz61DMEzDtvDtTWv4DgP2JgJrhK2LhERkXAtxLYfvQTbGuxc0tuFiThgLLbuvRtwP7Aj8GaEMYl0eNonPu7GuzuBOxnle9KJITSzNjV4YC5LeIPr3TdRhygRusq9D5zOWH8mH7MpNayDoxfNfAp8oKUVIiKxkFrb/BpWtf4Q4EngCOCDCOOSaPXEdjM4jHQthV9iRRFFpISUxIuxyuRh7iMvHclYlwDeSF5ERCSebgTeByYDWwIvAMcAU6MMSiKxETYjYyi2q0EDcH2kEYnEiKbTi4iIiEihngW2xzr++2KVyM+NNCIptwOwDpyhwBxgD5TAi5SVkngRERERKcYcrAL5ddiszouxPcE7RRmUlEUDcA/QG3gG69B5IdKIRGJISbyIiIiIFGsZcDLwE2zNfAM2rX6tKIOSkukM/BvrrKnDaiPsTfbCtyJSQkriRURERKStLscK3X0BDMf2Cd8+0ogkbAOBJ4BTgCasyGED2uddJDJK4kVERESkPR7AthV7AxiMVa4/MdKIJCw7Yx0zOwILSG83KCIRUhIvIiIiIu31Dpbw3Q10wdbLX4y+a1az47ElEgOw7QV3AB6JNCIRAXRiFREREZFwfAWMwEZqHVa1/m5g9SiDkqLVYh0wNwJdgXuB3YAPogxKRNKUxIuIiIhIWJqxNdPHA0uB72Lb0W0aZVBSsDWA+7EOGI91yByGddCISIVQEi8iIiIiYbsZK3T3EbAJth3Z/4s0IslnE+BpYD/gG2Ak1iGTiDIoEVmVkngRERERKYWXsHXy07AR3gewEV6pPAdhMyY2A2YDewK3RxqRiGSlJF5ERERESmUusBe2x3hqrfUN2FpriV6qdsE9WO2Cp7AtAmdEGZSI5KYkXkRERERKaTkwChgNrAROwKqcD4gyKKELcC3pXQQagX2Az6IMSkTyUxIvIiIiIuXQCBwCLAJ2Ib3/uJTfIOAJ4CSgCfgR6U4WEalwSuJFREREpFwewhL3mcBA4HHg5Egjip/hWAfKDsB8YH/gikgjEpGiKIkXERERkXJ6FxuJvwOb0n0NcDm2Zl5Kqx6YCqwNvIIl8o9GGpGIFE1JvIiIiIiU29fAkcDvsf3If4QVV+sdZVAdWB229r0R6ARMwkbkZ0UYk4i0kZJ4EREREYmCB8YCxwFLgAOxbc6GRBhTR9SX9PZ+HrgEOBbbC15EqpCSeBERERGJ0m3ArsCHwMbAc8BhkUbUcWwBvADsCywGjgLOw5J5EalSSuJFREREJGqvYPuTPwb0BCZjI8fSdodg+76vD7wH7AxMiTQiEQlFXdQBSAWo91sDh+HYClgbjwfm4ngJz51McDMjjlCi9AO/Jp04hAR74RgE9ML2kP0Ax7104lGucMsjjlJERKrffGA/4DLgh9ga7q2BU7Hp9lIYB5wDXIgN2D0BHA18HmVQIhIeJfFxdprfnVouxbNzxs/dt9eOxnEB9f4x4BwmuBfKG6BEaozvT4LfAKPxrBZ4X6R5fshy5jHa/54BTGSsayp3mCIi0qE0AWcBrwH/wNbLbwocAXwUYVzVogdW7f+o5O1GrENE+7+LdCCaTh9L3jHan0sNj6+SwLfGsReO52jwY0sfm1SE0X4XEryKffCvlufotfFcxRweY4zvX4boRESk42vE1nF/BmwDTIMCvrPE2zrYcoSjgOXYDIbRKIEX6XCUxMdRPRPwXAytjq1m44DfUe//UqKopFKM9vvieRRYq6jHOYaT4CnO9H1LE5iIiMTMk9g6+ReBAViCOirKgCrYbsB0YDts2vwBwL8ijUhESkZJfNw0+LNxnNrmxzt+Sr0/JbyApKI0+A3x3AZ0bmMLG9PEbYz0tWGGJSIisTUb2BMrdNcZuBoYT/5ZYnHSAEwF+gMvYR0fj0cakYiUlJL4OBnlBwL/1+52HJdptLXD+iuwRrta8OxDb04KJxwREREWY4XZzgMSWNJ6D9AnyqAqQB3wd9KdGrdgI/KqHSDSwSmJj5M6fgl0C6Gl3qzkpyG0I5Wk3u9AWPvyOv6g0XgREQmRBy4BDge+AvbH9j8fFmVQEeoHPIwVAfTA74HjURV/kVhQEh8XllAdE2KLx4fYllSG40Jsax3WYHiI7YmIiICNwO8OfABsiBW8OyLSiMpvK6wDYy/ga+z3H4sl8yISA0ri46Iv2wFrhtjiepzuNw2xPYma48CQWwy7PREREYBXgR2wdeA9sPXyxRbsrVYjgWeA9YB3gZ2Au6IMSETKT0l8XDSzXuhtNpWgTYnSeqG25lk/1PZERETSFmAV2C/BkvdzsTXhYSwbrESp3/FW7Hd8EOvIeCPKoEQkGkri46NfCdrUnuAdRYPvRvhffMKc+SEiItJSE1bsbjSwAls2mBql7khazjb4O3Aw8EWUQYlIdJTEx4UrwYnesTD0NiUajSwFloXcqt4fIiJSDo3APsCnZK4X7wiC6/6XAz8Afgw0RxmUiERLSXxcJJgdepuOj0NvUyLiPPBJyI3q/SEiIuXyNLY/+nTSldt/GGlE7bcH8CxWgf+T5O1rogxIRCqDkvi46MrzWAXTsHzGAF4LsT2JmuORkNv7b6jtiYiI5PYxVrn+BmwP9StI76FebRqA/2JL06ZhHRTPRxqRiFQMJfFxcYVbjuee0NrzTGasS4TWnkQvwe0htvYFy5kaYnsiIiKFWAZ8H1srn8CS4Ueonjo+nYGrSXc+3IQtFZgbZVAiUlmUxMeJ4w9YEZj2WkEdfwqhHakkE9yDWEGg9nNczDUu7DX2IiIihfBY1fpDgS+x0fnpwLZRBlWANYGHgFHYmvfzgBOApVEGJSKVR0l8nDS6N3H8M4SWLuQq934I7UilqeFsrMJve7xBN/4eRjgiIiLtcB+wI/AmsA7wOHBUpBFltw3W0bAHVhj2IKwjQkRkFUri48bzC1y7pjnfxUD+GFo8UlnGuedxnNGOFhbhOJzLnEYNRESkErwNDMfWl/cAJmFbtVXSd+DjgKeAdUnH+3CkEYlIRaukE5iUQ6NbyRIOgzasf/bcTHeO01r4Dm68uxrH8RQ/fe99atiD8e6dUoQlIiLSRguBA7GRbQecC9wJ9IoyKKAW61C4GegG3E965oCISFZK4uPoevcNjYwExgDzCnjEbDzfZ4I7XiOsMTHe3UwNw4FHCzh6JfAPmtmBce71EkcmIiLSFqk15idindSHYKPf60cUT09gMtahkFrDfwi2hl9EJKe6qAOQqDhPI+M5yd9AF47AcTiwObZmLAHMwfMycAc9uFvJewyNcy8B+zDGDyfBCGBvYCDQB/gUx7skuJ8Ek7jafRhprCIiIoW5EXgfm5G4BfACcAyUdUeVjbGZAEOwavoNwPVlfH4RqXJK4uPuevcN9oF2Y9ShSIUa554Gno46DBERkZA8i+27PgWbvv4g8GvKU0juAOAWoDcwBzgCK2gnIlIwTacXERERkbj5BKsEfy02qHUx0Ah0KuFz/hi4F0vgn8Y6EpTAi0jRlMSLiIiISBwtB04BfoKtma/HptWvFfLzdAauAf6GFbObAOxDYXWJRERWoSReREREROLscqyo3BfY9m7TsVHyMAwEngBOBpqw4noNwIqQ2heRGFISLyIiIiJx9wC2Pv4NYDDwJFbJvj12wToEdgQWkN7mTkSkXZTEi4iIiIjAO8BOwF1AF+A6bK18W74vnwA8AgwAXgN2SN4WEWk3JfEiIiIiIuZr4EhsxNxh+7jfDaxe4ONrscT/BqArcA+wG/BB6JGKSGwpiRcRERERSWvG1q5/D1gCfBd4Htgsz+PWAO7HEn+PdQQcDnxVskhFJJaUxIuIiIiIrOoWbBT9I2ATbFu4/bIcG7x/MTAS6whIlD5MEYkbJfEiIiIiIq17CdgZmEbmSHtQcKR+NrAXcHv5QhSRuHEzZsx4LOogpHiff/751itXrly9d+/eM7t16zY/6nha09TU1Omzzz7bxTnnBwwY8ETU8cTJ4sWLB3z11VebdO7ceUHfvn1fjzqebD7//PMtV65c2adXr15v9ujR49Oo4xEREWnNsmXLan79619v/Oijj64NsNtuu3120UUXvTVx4sRB11133QbeezbddNMv//a3v83s37//yqjjFZGOzU2fPt1HHYSIiIiISKWbPHkyl156KU1NTfTp04dFixYBcOSRR3LOOedQV1cXcYQiEgd13vuRUQchxXviiSf+uGLFis0GDx7858022+y5qONpzRdffNFn+vTpjc65xL777nts1PHEyeuvv77vvHnzxnTt2nX68OHDK3ZP2qeeeuo3y5Yt23LgwIFXDB06VLM1RESkoo0YMYI333xz5ylTpvxk0aJFtQDDhg27+/zzz78OwHuNjYlI6bmoA5C2qa+vf8o5N9w5d9T48eMnRx1Pa0aNGjWwrq5uDtDc2NiorukyamhoOA2YANzd2Nh4WNTxZFNfX/+Qc24/59xJ48ePvyHqeERERPIYjq13XwurYl8LLAdGA9dGGJeIxIgK24mIiIiI5FcPTMUS+FeALYE7gM7ANcDlWFIvIlJSSuJFRERERLKrAy4GGoFOwG3ArsD/gCOxreQ88CPgXqB3NGGKSFwoiRcRERERaV1f4EFsWzkPXAIcByxJ3t/yZwdg280NKXukIhIbSuJFRERERFa1BfACsA+wmMxR95ZSo/OzgI2B54CKrUkjItVNSbyIiIiISKZDgKeA9YH3gJ2x9e+5vALsADwG9AQmYyP4IiKhUhIvIiIiImIclnjfCfQCngB2AWYW+Pj5wH7AFViRu4uBm4FuoUcqIrGlbb/kW37w4K707j2QlSsTLF8+182atSzqmKRyeKhl003XolOnXiQSn7mZMxdGHZOIiEiIemBV5o9K3m4EfgisLLKdJqzI3evAP7D18psCRwAfhRGoiMSbkviY81tssRnNzadhHywbkkhAbS1064YfOvRNvL+TurpG99pr70cdq5Sf3267bixbNgrvRwB7AHU0N9t9Q4d+gff3Aze6N964N8o4RURE2mkdbLr8tti+72cA/2pnm43AG8B/gG2A6cDR2Oi+iEibaTp9TPlNN+3phw4dT3Pza8DPgA1bOWwznDuX5uY3/LBhf/ODB3ctc5gSIT9kyEksXfou3l+BFfVp2enXG+e+h3P3+CFDnvabbbZFBGGKiIi01+5Ygr0tMBfYi/Yn8ClPAtsDLwJrAg8Bo0JqW0RiSkl8DPnNNluP2tpngAYKm43RCe9/TK9ej/tNNx1Y4vAkYh5q/dChl+PcdcCAgh7k3K7U1Dzjhww5orTRiYiIhKoBeAToD7yEFbCbFvJzzAb2BG4HOgNXA+OB1UJ+HhGJCSXxMeO32KIPNTUPAZu34eE7UFt7n99yy+5hxyUVZOjQS7C1fMXqgXO3+WHD9gk7JBERkZDVYcXnUsn0LcBulG7N+mJgJLZFXQLrPPgvNjovIlIUJfFx09x8LbZ/aVttRXPzVWEbPUYaAAAgAElEQVSFI5XFDxt2FLa8oq1Ww/vb/JZb9g8rJhERkZD1Ax7GitY1Y4n194AlJX5eD1wCHA58hdWaeRYYVuLnFZEORkl8jPjNN98LOLT9DfkT/ZAhO7e7HakofrvtVsP7S0Joqi9NTb8JoR0REZGwbQW8gK17/xo4Ekusy+kebNT/A6wm0TSswLCISEGUxMdJc/P5IbXkcO6nIbUllWLZsqNpvcBhWzT4rbfuHVJbIiIiYTgGeAZYD3gH2Am4K6JYXgN2AKZiW9tNxvaUdxHFIyJVREl8TPgNNlgd5/YKscmD/HrrdQmxPYmabSMXlk6sWHFwiO2JiIi0lQPOxda9dwMeAHbEtn+L0gLgAGwmQCrGW7EYRUSyUhIfF5077wh0CrHFHnTvvmWI7UnUnNs15PaGh9qeiIhI8XqSOcr9d+AQ4IsogwpowtbkNwArsOJ3qdkCIiKtUhIfFzU14W8Nl0gMCr1NiYSHWmDtcBv12o5QRESitCFWOO4IYDlwCvBjrJhdpZkA7AN8Snrd/t6RRiQiFUtJfFx43zX0Np3TdK+OYvDgTnhfG3Kr2opQRESisj+WCA8DPsEqwV8baUT5PQ1sD0zHKug/BJwVaUQiUpGUxMeF9/NCb9O5T0JvUyLhPv54Kc59GW6jen+IiEgkGrAK8H2wkfjtgecjjahwHwO7A9dje9n/nfRe9iIigJL4+HDunZBb9NTWht2mRMn7t0NtL5EItz0REZHcOgNXk056bwT2BeZGGVQbLANOxtbKJ7BOialA/yiDEpHKoSQ+Jtz//jcTeD/EJl9yr776cYjtSdScuyfU9mpr7w61PRERkewGAI8Bo7A17+cBJwJLI4ypPTxWtf5Q4EtsX/npwHZRBiUilUFJfJw4F95aMO+vC60tqQzO3QisDKm1l93rr78aUlsiIiK5bANMA3YGFgIHYQlwR3Afth3em8A6WEfFUVEGJCLRUxIfL38FwlgbP4uVK8eF0I5UEPf66+/h3MRQGvP+nFDaERERye044ClgXeBtYDjwcKQRhS/4e/3/9u49Ts7x/v/465rdRE6EREKIU0WS3YlQ4nyK0qo6n6sOpSq0RasH9ESony/VVpVWQyl1qGMdSx1aoc4EjcxuohEpEZIIIedkd67fH5+5zb2TOe7ec9id9/PxmMfO7txzzbU7szP357o+1+caANyFbZmn83iROqV//jriEoklxGKnYuurOms1yeRJbubMlVH1S2pILPZT7GShK651ra097QRKRERqSwMWyP4V6EfHGeueKJxh4IBzgfuBdarZKRGpDgXxdcZNm/YQ3n8fW2tV4p1dO3C6mz79qcg7JjXBvfHGx7S3H0xniwB5/xjOaTscEREpp7WBe7FANnPteE8WrPU/DlvrfyCWhbBFNTslIpWnIL4OudbWK3HuaGBxCXf7GDjQtbTcUKZuSY1wM2bMoLFxR2x/3WJ54Go22OAAl0isKlPXREREtgJexIL2FcCJpKu414vbSFfd3xr7vN6nqj0SkYpSEF+nXCJxN86NxLlrgGV5Dl2C91eQTG7lEol/VKp/Ul1u6tQ5tLTsgvffxLm38xzqgSfxflfX0nKmmzy5rVJ9FBGRuvNlbL/3JtL7qd9S1R5Vz/PAOOzvMRj4B5aZICJ1oLHaHZDqcYnEB8C3/fDhP2DgwH2BbYEN8T6J9/NwbgqrVv1L69/rk4N2WluvB673zc2fx/u9sMJBA4jF5pFMziaZfMTNmDG3yl0VEZGe77vAr7G18M9iFdrnVbVH1TcX2BOYhO0rfymwJXAGoKw4kR5MQbzg5sxZzpw5DwLa11uyci0trwGvVbsfIiJSd/oAf8SCVIDrUJAathI4CfuM/jVwKtAMHEk0OxKJSA1SOr2IiIiI1KKNgKewAL4NW/s+AQXw2VwJHAAswrajewXYoao9EpGyURAvIiIiIrVmFywQ3RFYCOyHVaGX3B7F/l6twMbA08AJVe2RiJSFgngRERERqSXHAf8EhgFvYDPK/6pqj7qP/wI7AQ9gSxFuwtbK65xfpAfRP7SIiIiI1IIGLOC8BegLPATsDuTbJUXWtBg4FLgQcFjV+oeAgdXslIhER0G8iIiIiFTbINLbpHksdf4Q4NNqdqob88BE4FhsK+H9se3oRlexTyISEQXxIiIiIlJNI4HngH2BJVhl9fOAZDU71UPcjmUzvIP9nZ8FvljVHolIlymIFxEREZFq+Qo2QzwKeBfYC/hbVXvU87wGjMMK3Q0CHsEyHkSkm1IQLyIiIiKVFqzVfhBbq/1vLNB8tZqd6sEWAF8CbiBde+BWrPaAiHQzCuJFREREpJL6AH8hXTX9WmAfYH41O1UHVgKnAKcBq4GvYVX/h1WzUyJSOgXxIiIiIlIpw7FZ9+OBNuBM0kGlVMa1WP2BBcDOwCvY/vIi0k0oiBcRERGRStgNCxjHAR9iBdaurmqP6tfTwC5AAtgo9f1J1eyQiBRPQbyIiIiIlNsELHV7A+A/wA7A5Gp2SHgLm4m/D1gL+DMwCWisZqdEpDAF8SIiIiJSLo3Y2vdJQG/gTmBXYHYV+yRpS4DDsS39PDbY8hCwbjU7JSL5KYgXERERkXIYDDyKVaH3wGXAV4Fl1eyUrCF4bo4BlgL7AS8DzdXslIjkpiBeRERERKI2FgsEvwAspuNsr9Smu7C6BbOBEcALwMHV7JCIZKcgXkRERESidCBWgX4LbN31Lti6a6l9Qb2CJ4G1sedtIuCq2CcRyaAgXkRERESi4LDU+fuBdYDHsIAwUc1OSck+BL4EXIU9pxcAtwH9qtkpEUlTEC8iIiIiXTUAuBsrYhfD9iI/EPi4mp2STmsDzgJOA1ZjtQyeBTatZqdExCiIFxEREZGu2AR4Clv3vhL4BungT7q3a7G6BvOBbYFXgL2q2iMR0T6QAh5iNDXtiHPbAMMAj3Nzce41pk2b4lSEpu75sWOH09a2F95vjHMD8X4+sdgsGhr+5aZOXVrt/omISNXsgc3ADwXexwL5F6raI4naM8A44F5ge2zHgW8DN1SzUyL1TEF8HfOjRq1NQ8MPsT1BN+x4o7dLc/McD3+gb98r3ZQp2hKmzvh4/GC8/yltbTsADpeqa+OcvT7a2lb4ePx+2toucDNmzKhqZ0VEpNImAFcDvYDXgEOBd6raIymXd4HxwI3AEcD1wE7AGSjjQqTilE5fp3w8/gUaGmYC55MZwHc0HLiE5ctn+KamnSvTO6k2H48P8k1Nj+L9/cCO5K5K2wfvj6GhYZqPx8/3ql4rIlIPegPXAZOwAP52bGsyBfA92xLgKGyrwCQ2iPMEMKSanRKpRwri65BvajoB7x/FUt+KNRznnvRNTYeVq19SG/yYMZvg/Ys496US7taI9xcSj9/m9b4iItKTrY+lU38TaMcCumOB5dXslFSMBy7D9o//FNgTeB6IV7NTIvVGJ9t1xsfju+Pcn+jcUoo+xGK3+nh8h6j7JbXBDx/el2TyHmBE5xrwX6Wp6ZJoeyUiIjUiKGw2HlgMHIYFdFJ//g7sDrwNbInVQdBEj0iFKIivI37zzfvg/W1YGlwnG/F98f4mP3686in0ROuscxG2p2/nOXeuj8d3i6ZDIiJSI47GthjbDPgvth76war2SKrtDeyc4Z/YFoP3kN5iUETKSP9k9aRfvzOwbWC6qokFC06OoB2pIX7s2OFYgZoIGvOamRER6RkcMBFb994P+AdWK6W1in2S2rEQ+DKWkeGAc7HXSv9qdkqkp1MQX19Oiawl778RWVtSG9rbjwf6RNTabn7rrUdH1JaIiFTH2sDfgAuwAO13wIHAomp2SmpOG1YbYQKwCit+9yyweZZj9wP+XLGeifRQCuLrhG9q2gqIMqja0cfj+araS3eTTB4UaXttbQdH2p6IiFTSCKxg2aHACuAk4LtYMTuRbK4D9gY+ALYBXk59H7YzcBywXWW7JtKzKIivF841R9xiDOc009qTRP0aca4p0vZERKRSvgS8hFUcnwvsBdxU1R5Jd/EcMA4L4NcHHgPODN1+CLYt4V/QtrQinaYgvn5EP2vu/UaRtylV4TffvA+wbrSN6vUhItINTcAqj6+HzcSPwwJ6kWK9h209dzO2G9LvgElAM7Bx6phNiHKZp0idURBfP1ZF3qJzKyJvU6pj9uzVOBdtiqReHyIi3clawA1YsNUI3ArsA7xfzU5Jt7UC+Dq2Vj6JDQ69AAxJ3b4OcFHqay3YFrgzdZlQ5b6UwwnY//bldO8MiN+Qfp6iquMUS7U7CTg2ojbLTkF8vXBubuRttrXpg72HcNCO9wsiblavDxGR7mEjYDJwMrbm/TzgeGB5Ffsk3Z/HqtYfiE0mrU3HAHIw8Osq9CubDbGCfEcB21e5L1HbGLgGG5xYhj0v3dV+pJ+nqLa7TgKrsb/PJMqRvVwGCuLrRXv7K1j10Gg4t5zevadG1p7UghdrvL18hgGfS116VfBxRUS6u89jafM7Ax+R3i5MJAprA6cDK7Pc1hsrnKgaOvAV4JXU5dsRt30xtuXfx8AVEbfdU1wOLMZerxOr25XiRDWCITXOTZ++0Mfjz+L9XpE06P3jburUpZG0VRkDsH1ttwYGYaOQH2Jvli+jarvg3H14f0hEbbWzcuVDkbRVnFtJV8DdEpgVQZsx7PWyHTZLFXwATgeeSV2X7mUsNsjjgVer3Jeo9cJ+P4B3gKgza7qjPlhhNoBPgf9WsS+5NGBpvABzgHkVfvxjgeuBvsAMrOjYjAr3QXqmTYEzsDTuQVjAns362Nr5cRXqV60aTDoD4B8RtrsdcGLq+q/R9pC5fIjVbvgp8E3gamBaVXtUgIL4+vIbrMJs1zl3ZSTtlFcDlsJ1KrAvtt4vm/8Bl2Bbo3TnFKOu+eSTO1hnnYuwYjNd4/1NbubM7hpEjMZGwY/EZvizWQHcgqWcLqxQv6TrHgM2wGaEolpLVyvOwN7jVwEjq9SHTYE9gDHYiTnYYMKrwBNU/uRxM2ygFuBxrOJ6FLYEdseKdA3GUjHnYwPC/wSWlNBWOzYztge2XnhXKvM51AD8P+Dc1PcPA18DPqnAY0t92BcLhjy5A/jACOAY4I5yd6oO/QyblFgF/LHKfal1f8DO6xqwYL6m18criK8jLpF4wDc1Tca58V1s6j6XSPwrij6V2V7AfUUctxm2BmYfbO/S6JYddCNuzpzlPh4/H+//3MWmFtPYeEEknaqOx4HhBY7pg52c7I+9bjRzJcU4ElsPuhyIMlNlMPDz1PXrsYHJSumDzfJMIP860iXAVcCFZE+rrXUDgG9gg8Jj8hy3CPgVlo5e7GfJBcC/sHT2Y4DbO9/NoqyDDUIehAVYvwR+gg1GiETlhtQljg0yHoZNpmTbCWcg8FvsfbE7ZXnWumHYZBbYQJ0mHfKbi70XfxE4HBv0r3R2VNG0Jr7exGLHYil7nePc27S1nRpdhypmJnApcACWIj0a+wd9InTM0djMRN1yicSNeH9dF5rwwClu6tTOv8Zqx1LsRPfr2FKMLbEtcy4nHYRsjH0w9qtGB6XbuQOrqHtNxO1OxLYDW0Hl38O2wwZBCxWCGgD8GDtBGlDuTpXBeOBK8gfwYAHKxVgwkiv7K9OTqQvY51Q5s0S2wmb8D8JeLyeSrh4uUg4J4FvYsrSDsOVv77NmZs5Q4BcV6M9A7Dxwa6KpjD8Ie1/YHtic2oqtJpCuE3RLF9rpjWUejcP+brmyFKtpKFbfo4muvYcGf6fe2MBtzaqlF5pUgEskPsD7/YHZpd/ZzQC+5N5888Oo+1VGL2OzpSOxE8iHsTUuM4B7sfTK8An12aT3MK1PsdgZwG2duOcq4JuupeWuiHtUaR8B52AnHCcAf8FeR7OAf6du+zJWyRSsmN63Kt9NEcAGl05PXb8R25+5Wp4DvocF9utjBYLGYdkBgV2JfhCjkjwWcH8H2AYbPFkH+73CM+j7YTPcxQoGXzZLtV0OX8b2e2/CBvP3oGsn9iKlSGL1ZI7Hlu0dBTyIzQ63YzHJccAWZXr8bbC15guBqanLh8BdFM6+y+SwVOsXsSVDb2BLd94GPsDe4/IFuv1IF7ELLheFbv9GltuDS7EDDw7LGATLhCol+8th7+PnY+8ZS7HBmJexv9tcLI64Agueq2kX7HU1D1u61YI9rzfSuSrzfyM9UVPTk5Y50+k9xIjHt8f77fB+CLHYWsDHODeN1aufdzNmLM56v9GjR2If3B01NCxn9epP3Jtv5jzB8MOH92XAgGYAevd+102dOj9f53083pv29q0BcG6ua23t1JZWPh4fgPc74f0WwKY4twLvZ9HQ8KSbNq1m0yg6y7W2TvMjRuzIWmtNwvvDiriLB26jV68z3Ouvd6eCGM8DO5F/faEHfoS9Ga+LjVgeiM0s1SWXSKzycDzx+Ct4P5HiPjBacW6CSySeKXP3KmEXbAuWfCZjJ78np74/jNrZJkfqy+mkP8tvrFIfHsbWD76e5bYp2InkW1jtEbAT9UuA1or0LhoeuBs7qc3W7+dTl3exzxSw2hqXYSe8hTyZuu8mWBB/BdHOjn8Xe49qAJ4FjqCG00Slx2vHMiGfwIoqHoyl3G8HXIulM0fpMCwTKnP3ml7YMqc9SS9JKqQXlk1wVI7bh2Dvy1/FKs4/n+WYGPmzl4aRexCgobhuEic9OPEypS1j2hh7785nM2zQ9iTs+ft3Ce1HZX/such8XvtjWZRfwZbWlvJZswT7LNsJG1AaCbzZ5Z6WwRpBvB87tj9tbWfh3Nl4PwQA58Cn4iDvoaFhhW9qupdY7GKXSLR0aCAWuwb4whqP5D00NuKbm+cD9+L9/7nW1o7r9gYOHIn3VoSmre0sbP1cbs5tQCz2Sur6BXQcxSrIjxlzOMnkiXi/H9AHF9q60jlIJlf7ePwmli//oZs1q0cVe0kVHTvcjxmzK96fjvcHYClBoYPcArx/kGTyD2769EL/zOUwCltvHMfeFMFG194EHqHwOuRi97ddio2m7pf6fkRp3ex5HHgSiSv8yJE309BwFs4dxppppKuBp4DbGDr0Zjd5cq3VEtge+5DdFOvrDGzEvVCF6kIBfOAJ0kH8Vp3poEgX9cFOoMDeFyu5rWPgBWyZUiGXYyfpG2GzPAfSvYL4R4C/F3HcROA0bPCzETvJvD7fHVKS2MnoediJ45exwZGu6oMNSgfVqa8FzsQyp0RqwXIswL4D+78Zm//wkjVh2YVBoPck9j/xHpb+fia2ZO7SItv7NekAvi3V1sPYDhhx4Cws9Xxd7H1jDGsuY12JvU+E7Uz6nOJh4P4cj1/sOcr40PUXirxPpnbsPO9J4D9YFsN6WAB/NBYgr4u9N34eG6ytpEnY83o78FcskzIO/AA7LxuS6tsYiv+7gX2W7pS6vhfdIYj3o0ZtQVvb34GmVNCexPtXicVm4v0yvB+KczsAG+DcsXh/lB89euesAZ5z7Xj/aegnA7A/9FDgNGKxr/qmpi+71tbOvrC6Lpn8OemtXZYArwEf4NxwvN8G6If336RPn3F+xIi93MyZn+Zsq5ty06Y9Bzznx49vZMGCjXBuOG1tnsbGOUybNtdVZ+u1H2LrePIFRldgRevOwmYvuiqcWeJyHlVnUksnzgfO9yNHrk+vXhvj3HrAPNZa639uyhR7U2xpyddMpfXCTlSzpUFdhFXw/jFdf22HK1DrNVMbtsKWQGyPDUq+j51E3UppH+CBXbEZnGYsPfwTbFnFI1hqYr7X0BdZMy00eJ30x97jsnkeS80sxpGkq8B3Ji26Eauyvi/2Ow7FZogWYoNdj2DV1vPNCBc7W9yG/W5HpL4vV8psIUOwE+XdU9cXYDNIN2NpsLkU+3suw2awgi0vS/k9gx0vwGbyuhrEb4wtG9sB+/v/DO3/LrXtUyw1OkpXkF4jfSv2GRFkaD6DBYAPYANuhWxLerlLOzYD/Ujo9meAm7C0/b2w9fe/wmblw1Zj5ylhy0kH8a9lub1U4d2oSh3gXY3VALmc3Eu0rsGquZ+OZWCfR+XTz9fDssAuCf3sGSygfxYL3rfAgvpS6i28gMUXYH/HrtSKKpvPgng/duxQ2tv/jffBeuBJeH9hZoq6hxijR++BcxNxbjwNDdlTbb1/y7W0jPrs2/HjG1m4cATt7d8DTsP7gTh3p99885Fu9uwVZfjdiuGBB/B+Ehts8Fh4JtGPGrURDQ1/xIpwbEuvXhOB71epn2WX+t3fSV2q7VA6BvBzSL+JbEo6xehQ7A11T7oeyIdPtCo9ktgtpAL67lAP4WIsuAEraDgPWze8IZaG9iPsA6er69j1mqktP8Ke+8ytjA7DUv4OKqGtwdiJWK4Z5tOwdYHHkzvgPo10wJppPXIv2Tk7T5uZwumcpe4rfBE2M75enmPOJp0Ony1VvlThdM7VOY8qn92Be1hzDedBWHX2CVi2TleFf89SMpQSpFPq98fep7IuXSzCLtjazg2xQZmjsaKCIvVkc9JbSy7GlpVkLrFsw96vZ1J4K7xTSdcTu4mOAXxgBfae2YrFWdWqcr5t6HqpO+jMwz438/HYEoSgJktUW3iWooXsGRSfYs/1P1Pfn4LVHSl2MHZ66Po2ne5dmaUL27W13RgK4M9yLS2nZ1tj7iDppk9/yrW27o2NUhT1QewmT25zb7wx3bW0nI5zf0n9eBP69SvlxCpa3h/mWloOca2tD2emArsZM+bi3JEEJ+fOnehVCLCSnsfeLIdhJzQ7py4bYQFZUFV+c7q+7+Xm2DossH/wB7rYnlTXkdgM7J7YYNDu2IzUKaTfr07H0lW7IlxPopitDKV8vocVEQtOwN7GZiAfwZbLNGPrmTPXzWUzAAt2ggDeY8WE7sJSCoM05LGp75tytDMPm7UPXwJtWW4LLsUu3YphhcnAThr/U+T9AruSDuBXYsWL7ktdXiGdubA99veIIsU1vH99pdMTN8CC2qHYc/NA6hIMTK6Lpdx+JYLHCg9Cl3ryHMyYNQK7dfLxj8Oesw2xwaYdUAAv9ekrpDOg8m2x9i7pgK9Qe4F82/HOBJ5OXe9FdQLcIaHrH5XpMT4kXfNjOIUHQaJ2M7kD8ydJ920zLM2+WOHXSbUL9+XUCODHjNmVZDJII7nbtbTkX4ueUuxxa0gmb8G5YH3WjkQz8l2yNdbkZ96eSKzyzc2PY0HjYMaOXZ8CxfYkEqeSf63kLOAQ7E1yGDZjsRHFFQ/K5v+RfpO/g+pWd5au89gM5bOhnyWx/WrXJ51Oej6lz14G9iOdqrYo1bZUx1bA/4W+/yG2ZCKYbRmEpdYVexJ1CemA9T1ssObl0O0bY+8Tu2Ez9n/GAuLME4lsFcaDCswfYJ8rXTGWdBD+KqWvcfZYwH4D8BhrFj1aB/gdVhxoPeBP2Od1Z21JupBTEhtkqaTgOb0KS60MBvTWAn6PDfI1YhkSTXRcLlOKcaSf2xUUt44+7AXSmUR7Utp7VAP2eXZu6vuHsIC+xy0FFCnS50PXCy3ffZ78KfWDsUkfsPePQinqz5KuEbYdFnBWSm8slR/svf7jLra3HjYYPpg1t84M6k/FsEHwcg0YZJOtaGDAY89RMOHyeYrPcgv/DoOx363mtuG0meX29vDavEuyHxrlo8be/uy6ra+tXd6nZ0VWrarG+vB6VEyxo2Wki344Op/ucghWmR4s1ercPMdK9/BPOgbwYVeRPqHdmc6ty12Pjtkf52PraqU6zia93vFGrOhQOF3yIywoKmaQbzDpYkMeOIaOATxYYH8w6RncnbA15ZW2e+j6q524/3HYyc2DZK9a/CkW2AZphTsAozvxOIFfkh4s/QudH3TtiqewFMtwBmFQYCp4nodja2Y7K7yt3B8oPYAO1xjaPedRaxqEBfznYq/dy7DPNwXwUs/CW8fNLnDsrAK3h7cffpfCmcjhZXalbmHXVeFC1cvo3PKlGLYs4BXsc/QZ7Lz7zoxLeEC6K/uzd0ah5+zt0PVSnoMlpP9mDVimVs2xIN658amvb7uWltcq8LibfnYtHCTXIueC2bZ33PTpudJwpDrCmRQbdeL+n8Nm0YITy1OIpkieVFe+vVCXY9vDgT3vu5TYdgxbB7d56vsHgKtLbEOiE8PW+gZy7cu9GAuoCjmSdDrgZHIPBn1Ex73Ojyui7aiFT0g6kyFWzH3asSA/sHUnHgfs73N46vpHWGHJariM7FuOtmPFrwLHdLL9M0kXtHuPEnfMSQkX1yv2pHMU8Bw2mLQEex2fRw3OHIlUWHjL60L1JQoNeIVrgBVTqyLc3sCcR5VHOJOoD6UvB+6HDQpeR8et8IJZ/fAlvBy50suOC2VMhZ+nYrZLDvQmvfzOF/E4VRHzW28dbBUA3ndmNL8zjvzsmnPV2LqsKL6p6SvYbB3kX/si5TECS429FVvP90rGJZyu2rfEttfH1kcFmSCXUaVlHRK5aQVuT4Sul7JGCmyWN6jj0YKlGmcLCqQytsJmz8EG4PJl8RSTlrxT6HqhyuDh23fKeVT5rB+6Xs70xfDyog06cf9m0pkrSawYYL4q8OWyGlsjmctjoevjKH4v5sBOWBXq4LGOofj6BmHh53JIzqM6Go7Nhr2LLfP5WyceV6QnCg9kFdpFptDtpX7Wh9ur9HnCEmw5D9h7WamDCL/CdlgBqyFyJpa5uBY2yx++RFH0tFzCz0Ep2dSDQ9c/pUa35GwkmUx31LmyVk70o0cPpqHhe3gfVDKcT40WEfOjRm2Ec8GWArNw7ld57yBRGozNbmZuyZFPKcU0+mPrFIPdE26mejNDEr1C72Ph1PdSlvOcR7pa63tYgZtFJdxfohcuKjc951GmBTuRyneiFm4vkfOoNW/fCltPXUol8q6KqmjR7lha/eexIqJr0/H9NLz+sdTB0uFYccEBqe9/SvZqzpXwFumT2mwWYuckQ7HPiE0onH4b2Ao7lwn+bmeRO4ujkI9Iv04HYH/z5XnvYWElsm4AACAASURBVEuIjsYKaSljUGrFDdhgZ64lO5UQnoldO+dRptBMbakz6+FjqpF1vAB7HwMLtotdFz+I9FZxn2I1X/KlrVcz1Xwd8v9epWZPBMJBfM3WQmvEPqyM953ZRzeXLXxzc3g9yDrA+qn95wFW4tyJLpGouRQFP2rU2jQ03IelaK/AuWNrsZ89VH86VkJejZ2YtGLBWXhdzx6kK0gXu093L6xKdVCg6REsjV6zqT1HoS0rw+9z/XMe1dFJpOuFfIQVtstbGFMqInySVGj7w+XY7ES+E7lwe4XqHCzGXmtBquJAKhtAhWeKO1OvZTi2Nn3vQgeGFFPdP7Ae9v4aLJ+7iuxbAVVKMdtjfki6EvF6FBfEDwMeDd3vQrq2Y0qSjoNNxWYEVLpQoEghHrgFG+yejO07nq8QWTmE10R/rsCxIwrc/i7p/81NsAHOfIMT4fZK3b652HPafGaSDuI3ofitcHchvQX53eQP4PsQXiJdeVuS/1ws/JwXWj8fFv6danYL4Uba2z8h9tkShkKjVKXoRfZ/mJXAQ3g/0bW0FEp7rTg/dmx/2tr+jhXxWY1zR7tE4qVq96uO/JB0AP86tvQi1z9QA7n3cc4mWM8cbC32HFbFvBr7FUv5FCqsEp5NLGZw7hBsXZjDtis7kMKztFIZ4ee6mHS3leT/nOuXcWwhQRAf3LeSQXw4KB2U86js1sFOqoOCRJ9iler/C8zBXueB/bCBTij+xLIvNvs2JvX9HRTec7jcin19BIop0DQQG6gICmROAiaW1q01rEd6XWkw8CTSHZ2DfV5ugC0v2Q97Pd+K1RSpxEB4ONV7V2xJXC6FCkkuwgLBLbHzz93Iv3XjnqHrxdQbC09ARLGG/mnSg7Q7kq4HVEh4qVahpU9foPLbyoXtTu7nIEZ6STQU9xwEwkvkns55VJU10tDwId7byJJzUVZPnIP3J3/2nfergY/o33+6mzIle9DknA/N1Bc+WWhvj+E+O6zLM6l++PC+tLXdD+yBc+3A110i8WCh+0mkghT6oDJ0vhGwUtZnOqywVVCJ/nVsAGBpzntId7UB+dfFh/f8LBR0fQG4HRuVXgkcSuVnEiS3cIBTTKp3vwK3h9PtSm2vlFS9KIQzBUoN4s8hHcA/i6XT58o82KzEtnth9UWCPc4fBU6k+kXWon4++2EDFcHOKHeSfVvBUoWfS+16Id3ZQmw2OPjMXTd1+SFwMpamfDW2BWi5dlH4O5ap1IBtH7cB2ZfcjSC9bWw+D2E7XIBVbs8VQMaxQQOwAcRHi2g7/P++VRHHF/JU6HopdVvCz8WonEdZkPyTknqUdjDpHabAMjY6syvICViWZLalbPuTfu21YIPUxeoeQbxLJJb45uYWII73O3hwLprU4mWutfWJEu8TDqgKp7nGYv1DQX+XRqt9PN4b7+8B9sFONr7pEom/dqVNKVmM9BvXbODNAsePK6HtS0lvHTUDGxHWeuaeaWtsjWgu4WJ2+YL9nbEPmT7YB8RXgVLf06S8wrPRGxY4dl0KB/HhteUb5zzKDCE9A7GKym/lFZ4hWT/nUdkdkfrqsQA7X7BYys4fDViKfpAh9QxWlb4WigINK/GYfH+T3lia6R6p7x/GTiaj2IY2PMhYzgKAW9BxlkqkHLK9hhuw1/lQbJnNL4H/AP+HFZiMcjvn97HP8cOxgbxrsQzP8GRiX+B60ink+VyHDdY1YhNND2AD/WHrYNudBhk1d1Lccp7XsXONRmynid3ofG0NgBewbJ6+lPa//hLpZQMHY5nJmVutNgC/IT1YWy0jgP/HmttDD8WWbwSuoXgx0ktul7Dm714z7AXr3BN4HweGMmbMF5k27bH8dyuTZcs+pE+f4IVTePQ/mdzis5l47ztdeMBvv30vli+/Cxu18Th3ukskbuxse9JpvUiv/yu0tGMkxW8P9hNs5gksfWtfarhQhXTZAcBvc9zWFxifup4k96z6WGwEf0DquJOxdGOpLW+ErsexD99cM77b5Ph52FTSKZDbkn+d8bah6y15HjesHetjFOmHL4auF/O7BRzppW5zKbxOsNiTP4edKAXZVK9iqbRR1trpik2xVPVcRZA2I10EaR65C2Q2YLNG+6e+fxoLCqIaqAi/rl7MeVTX7YkFGiLV1Ct12QPYDgtijwIej/AxfoRN3PTHgtIXsGB8DvZeeDpW1DQobJlPAgtez8Hey2/Ddqx5GCteNybVXhDDfEz6/LOQRdjg4FdTbT+FLft8m46p9mdT3PvqcmwA4evYYOxOFPee8h62w8UR2HPzFPB77O+2FJudPxn73Pkv9rk2uoh2y2E+9vfdFsvo+BCbyPkO6YH4qdhSp2LtTbro8e3UxiB0VhbEJ5O/JxY7A+8bSCYv9PBPV+RImN988z5u9uxChaSK4mbN+sQ3N/8XC9AKp7XEYnt9NhOfTHZqpMRvv30vli27HecOBjzen+FaWq4reEcph5XYqOkwbGbp82Rfw9KIvaEUsz7zO9goHdiI8L7YG7f0XPtgaWzPZbntTNIn6k9jhWoybYWlvg0K3eeWiPso0ZiLpWuOwKrJ7gb8O8exhxXR3rPAGaHjJ5I7M+3w0PVnimgbLEV7EPb+1puunRy8hJ3I9cNmSvINYGQK3juD9de57jeG4oP4X5KuaNyK1R6JqiLzSXQctJ2EDRKUIoadbP8lx+0Hh67nSp90qcc+KvX9K6k2C1WPL0Wl0jjfJ/96XpEobELh1HCPZTK9hQXIUb/uZ2Hv5/dh75fbsebM7NvY+/1NRbT3E+zz5hTsPeFrqUum+dguNu+X0NdgK7edsAHDPUhn/ATOo/jB0T9iQTzY9p7FDgwGAxvN2OTHD7McMwf7HLy+yDbDMrPHCu0uk8tJ2MDHl1KXTLOwweRSal8dH7relSKlZdcI4Fpb/+vj8UnAt4GdaW7+g29p+Xa+QN6PHduf1at/Tyx2E/n3Xi3VPdh2X6N9PH6wSySybkHnR45c/7M1995PddOnF0q9XrON8eMbmT//Fpw7PNXO911r6x8633WJwAOk095vwdawTw3d/jksgN8XS3MZQG77A78LfX83tsb5CwX68D621lG6J4etyf0q6YAuhr3ZXxw67qIs9+2LzQAEqdmvYDMDE4p43L9S+XXRYu8TE1PXL8TeGzKD0i1IF2fL534s8ByIjeafQPagrwl7PQWKOfEDW8qzC/Z63Jvi1knmsgqbGfkClrnUTP7lIQGPndiMwk5o96XjHumBvljAWsxg6c9In+S9je0vHOV67r3o+Pd+jNKDeLCT73tYsxbKQOAHoe9vznH/X5N+HSWwgYqol1EEgyae8gbxj5H9eReJ0p3kDuI/wv5/bsBmxsu5fORxLMPuYmzALlhatQT77D4PqxMSLJlrzdNWO7Ye/h5sffxedCyE+RZ2vnkZxW/rFvgQm4TYF/uM+Bz2/h7eGaSUgPQFLE1/W+yc6PtF3v9D7L3ox6Rn8gPzsBnqi1PHvUz6fbDYSd3xoesLsWUVxXoeG8AHK9a3C7Yu/suks3k/wM4NLqK087K+pAfoXwKmlHDfikuv/1i69Af06zcOWwcwgebmsR4uYtmyJ8Mz7X7MmE3w/kja2s7BuQ0p/uSlOM79Du9Pxbaju8XH42fSp89t4WJ4vqlpZ5z7I8FIjnPnd+qxFiy4AdtbFeBWvP+3Hz16+5zHNzTM0FZzZXcx9g80BDspfZV0xeQNsZRZh50M3w5ckKetJtJrkiA9w1bI0yiI787uxtJbn8ZmaT/APpzD611/R/bBx/50XMozjuJrLzyGgvhquBLLuBmCnfTcggVkwezH7sCfyT/gF1gO/AL4Ver7San7/Tl1Www7UZhE+qTtXmywpxh/Jz2jfBu2BvRF7IQkmPFfSPEz2PeTHpTch+KCeLCTz6Ag0Z+xGfSHU987bBbot6mvH5J/zf1B2N8s8Ao2m1TIu9iAbCWNwgpTTSBd5KgJm0kK/u9fSB2T6etYGmvgVSxNt5A3sSClGJuR3pbqBbTsS7q3GOniboFPsPfSh7H37qmZdyqjt7CJoV5YgTuHfU4ERdE+wgYgi/VI6tIb+/zpi/3PdnVgL0m0g2y/xgYm18eK895V5P0WY58TP8HOvwdgf6OPMo4r9tw6LDyZ9ktK+5t9M+P7qdhsex9ssGEZnR8QOoZ0tuZvOtlGxXwWxLvZs1f4eHwfvL8Ze5J3Bh6mX7+Vvrn5HeyPMpRkMnwi/C7JZKSpyS6R+MDH40cD9+L9QLy/keXLf++bmt4iFluN95uQXrPicW6iSyTuz9Nkbt4fFPruOGKx4wrcY3e6VmRCCpuDpcTchZ3MNGBrbcLrbSYDx5GuNC8S9jPsROEU7DUU3qu1DfvA+HkV+iXlsQgLsO7HTs6OxQZn52InVUEA+jcs3b7Qrha/TR13GHZS8HvsJOh9LIVyndCxrcC3Sujr1am+boWl1V+e5ZizyV3TIdNfsIGAfth74pX5D//M5djfaAR20vN37IRtDnYyGvzNXsO2g/pVljYCm2R8f1TWo9b0EsUH8ZkFpzqTvv4MVtxwPDYIHAzyhGeYFmAz/tmWUGT+nsVWUv4HxQfxx5HOfKjpNE6RIhyBZbksx95fpgJXYBlIURavK9Vqol1WuQpbR16rbgXOwpZdXYAN4pa6W0iUWRKjSa9XX4DtHBWFFZS2F3ymBiwjA2xw/c4u96jMOnwwpmaZD/Px+H7AD/B+D+wkJpwK47Eqkn+mb98/uSlTMtdlLAE+xvtOj0S5ROJJP2bM9ng/EeeOwPv+ODc2VIneClI59wuXSHQlHXERpVTi9z7bFgYSvdexWfhDseI7G2Hpj+9iJ5vPY8/b3dhrEbJvHXE3nRvlVdX67udHpAuRvION1F6LnURsjtVbaMEGh/JtW/gJpY3Eh+UqhCXl9whW0PB6LNhqoGPQdSc2qDOziLbasQD3Z9jrqh/2ObhF6BiPzfh/n+KqDgc+wQbIf4YFu13d1nURlpH0DewEbTTFrS1chM2E3EK6kN/a2Kw0pN9fJ2CBZbXtELo+i84VvVqOrVu9G8s4zKy6PwWr1D+jMx2MSPC3Xkg3OIEUKeBy7Lztd1jmUaV38BDjge9hA5lx7LPnjir2JzwLfzFd3F0sQidg2VoeWx4WxU5tZZV1O4VUYPyo3377fqxc2Ux7+1AsXeQjYrFpLpHITKVI37el5ZAoOuamTXsLOMHH46cQi42lvX0DoBfOLaShYZp7441S15ms+RgtLVsUPkqqZDUWcOVL+/lf6pLLO6mL9HzZ1i29lLqUYjXaRq67ehwbcN4PK1w0GJuNfwQbGIR0saBCH85t2Dr732KZQXFsdnoRtub7UbIXRSzGR1jw/30scB6ScfvCEtu7BgviwU5Cflrk/d7F1nLuhJ1UDcMC3XexVNdgRuNm7G8Y9D3TLdhsc6lWFnncxnTcq/hCil8TOgtbSgP2u72PLWcYjxWLWh8bfHsKy7LLNzt1NRaIlKrYAlTbY4PXYEscIikYLFJF2xBdcUvpmuewwH0/bFb+TqoXpO6d+joHm2ypBTGsLtzH2HLaYovVVlXePRFTs+zFrvUrC5dIrKp2H0REpFtYiRXHzFoQlfyDftksorwzoovpeh2FV7D0yCOwk5DLKS2b6EXyVyz+lPwzaIVu76rwrM2blBZIr2bN9MokVpW91MrsiyhvllawDdUirCCWSHenAL621MoS1AuAS7EstloZrEyS3hu+24gVPkRERERq2DnYAMa62CxLT7J36PrPSReh6kniWDFOsC1RS1miISLSnbRg2ZOlDqpLhrwz8dLz+fHjG5k3b29isUPxfixW9MkD83DuVeB+EomnXOlFMKSH8KNHj6Sh4TC8H4+t4R2AVWCdjXMPAw/mW2IjImU3C0v3/gGWpn8VpW9tVKuCIH4atp69J5qITarMorStlkREpE4piK9jPh4/gvnzL8G5kaGigYGRqcKG3yUef8N7f65raXkkSzPSQ/kxY7YkmbwEOArvM/eK3hzYEe+PBhb7ePxX9OnzqyyFLkWkMi7G0s3B1nr3hCB+XWwP4pexbf164mByI1Zj4XFsdqrYWgEiIlLHFMTXIT9+fCPz51+B98Xt7ej91sDDvrn5WoYO/Y6bPLknpjNKiI/Hv4z3t2PbwxSyNt5fyLJlR/jRow9x06fPLnP3RGRNi6idIkFRWYTtFNCTtQF/qnYnRESke9Ga+DrjIcaCBXcCxQXwHU1g3jydbPRwfsyYw/H+73hfTACf5txYYrFn/ciRGxc+WEREREREOkNBfL1pbr4A7w/r9P2d+7qPx8+OsEdSQ/zo0VuTTP6Fzr83bERDw30+Hu8dZb9ERERERMQoiK8jftSoLYDzut6Q/4VvahrW9R5JzYnFrgD6d6kN58YBEyLpj4iIiIiIdKAgvp40NPwciGKGtD+x2DmFD5PuxDc17QnsE01j/ud+++17RdKWiIiIiIh8RkF8nUgFVIdG2ORRHjIrlkv3FmUBqaEsWzY+wvZERERERAQF8fVj5codgPUia8/7jWlubo6sPak+574YcXtfirQ9ERERERFREF832ts3jbxN7zeLvE2pHuc2ibjF6F9zIiIiIiJ1TkF8/RhUhjYHl6FNqQI/dmx/vO8bcbPrR9yeiIiIiEjdUxBfPxZG3qJzH0beplSFmzp1Kc4tj7ZRtyDS9kREREREREF83WhoeLcMrb5ThjalWryP9jUSdXsiIiIiIqIgvm6stdbLwMeRtefce7S0tETWntSCxyJtzftHI21PREREREQUxNcLN2XKauC+yBpMJu904CNrT6rP+7sibG0+/fo9FWF7IiIiIiKCgvj60t7+C2BlBC0tIRb7ZQTtSA1xra1PA09E05i7KDVwJCIiIiIiEVIQX0fcjBlvA5d2uSHvf+YSiQ+63iOpOd6fDSztYisv06fPtVF0R0REREREOlIQX29aWi7CuXu70MKNrrX1ysj6IzXFtbZOIxY7EUh2som5tLUdpll4EREREZHyUBBfZxwkGTLkaOCqTtz9WoYOPTXqPkltcdOm/Q3nDsC5T0q6o/dTSSZ3c2+++V6ZuiYiIiIiUvcUxNchN3lym2tpOQvvD8e5GYXv4N7AuS+7lpbT3OTJbRXoolSZSyT+QSy2Hc7dQeEChotx7nz69dvFTZ8+uwLdExERERGpW43V7oBUj2ttvdePH/8g8+btDRyCc2OBYVjQ9j7OvQo8QCLxlOt8erV0U+6NN2YBX/WjR59PQ8NheL8XsAmwNjAP594mmXyYxsYH3RtvRLd9oYiIiIiI5KQgvs6lZtYfT11E1uCmT38TuCx1ERERERGRKlI6vYiIiIiIiEg3oSBeREREREREpJtQEC8iIiIiIiLSTSiIFxEREREREekmFMSLiIiIiIiIdBMK4kVERERERES6CQXxIiIiIiIiIt2EgngRERERERGRbkJBvIiIiIiIiEg3oSBeREREREREpJtQEC8iIiIiIiLSTSiIFxEREREREekmGqvdARERERGRCjgIODDjZ5OAV3McPwD4dcbPZgC/ibhfUfo2sClwEbCsyn0RkTLRTLyIiIhI/TgEeCt1+X6W288P3f7F0M8fCv08uLwB3AZ8pcDj5LpE6dpUm28C6+Q4ZhiwfepyMDAB2CJPm42h43dOHZ85CFBrjgfOBfpWuyMiUj4K4kVERETqxwDgc1jw+o2M2xzwzdRtnwP6h24bnvrZKuBj4BMsKD4W+Dvw+4y2guM+Blam7rtR6GcfR/ULAX1S/dgC2ArYL8dx1wLjUpc/F9HuotDx2QYqRESqQkG8iIiISP15CYgDY0I/2w3YJHVbLidjQe12wBAs6E9iadz7ho57hHQAfELqZ7NDPxvX1V8gZG9scOKe1PcHR9i2iEjN0Zp4ERERkfrzJDZzfTQwLfWzY7BA+yVgpyLa8MD1WNr9MVgK/RNRd7QIh6S+/hbYEZs1bwTaqtCXwBBgbWAusALYEhu4aMP+vu9muU8fYHcs62Ex8G9gfp7HGIQNYPRLtTkjx3EOe65XAXMybuuLZVR8CnyY4/69seUEm2Hr7FuBljz9Ggrsmurfu6nfY0We40WkRJqJFxEREak/bcDfsOAboAE4ErgLC85L8Z/U12HRdK0kDlunvgh4EXgMCx53r0Jfwi7D1ujvDFyFrdW/Hbg79fP9M44/Hgt4H8dS/e9Off/THO0fB7yTOu4vwHTg8hzH9kk95kNZbtszdduFOe57PPA/4KnU49wNJLBBg4EZx/bGllW8B9yLDfA8BszEBhtEJCIK4kVERETq0x3ASODzwHhgw9TPSrVJ6uuiaLpVknHAxlgGQBvwj9TPD8l5j8o6EwuEfwl8FfgO8C9spj5wAhYg+9Ttu2AZEm8CFwOnZ7S5I3ATsBo4BfsbfAs4Ddg2wr6fAtyMBec/xAYkdsEKIg7DMg3CbsWWVUwBjsBm48/GCg0+iGUjiEgElE4vAPjx4xuZN29jGho2pq3N09g4h2nT5jpor3bfpDb4ESOG0KfPMNra1qOxcT5rrfU/N2WKtq8REem+ngbexwLGwdiM7BTgxBLa2DZ0/JOR9q44wfr3R1Nf/4kF8wdjAWS1fQlbmhBOP/8D6erxA4DfYanu40PHvYD9PWdiOwb8ifTygJ9hmRM/AG5I/WwKNjmXWWCwswZiW+mtBPYBXg/d9gI2iBBOkf8KlsnxGrAHNsAA8DyWwn8XcB5wakT9E6lrCuLrnI/HdwNOY/78A3BuEMkkxGKQTEI8vsB7/yDJ5B/c9OlTqt1XqTw/YsQQevU6E+cOB+IdXh/Ll6/2zc1PAbcxdOjNbvLkaq49FBGR0iWx9OhjsNnSa4u4z7eBQ4G1sFn8LwK9gGfp3Cx+V2UG8UFa/W5Y0b5p2e5UQTeQff348tTXA4B1sVnszOM+xCr/fw3YBgvUG7ECgstZ8+99C3AFNnPeVQdhr4nb6RjABz7K+P741Nf/Ix3AB+4BlpJ71wARKZGC+Drlx44dSlvbJLw/NPdBfgjwDWKxk31z81/p3fs77vXXq5EqJxXmwRGPn433F5B7v91e2InEvsyff45vbp7gWlr+XbleiohIBO7AUr4B7izi+BMyvp+NBaCXUPlCcpsDY7HgN1wo7lEsiD+Y6gfxrxa4/fOpr+th+7tn2ij1dTMsiN8Em8WfiQXGYZ9ihfQ270xHM2yT+vqfvEelBb/Hjth2gpmWYQX7ql1wUKRHUBBfh3xT0xja2h7CPhCK4YCvsWrVOB+PH+ASiZll7J5UmY/He5NM3oj3x5Zwt9HAE76p6duutfX6cvVNREQi9xyWgr6C7DOumY5IHbca2+t9Sfm6VlAwC/8xMCH080Gh2y+paI/WlK0KfVgwUL4Xtt48m49Jn7P3C/0sm4VEE8QH693fL/L4oMjdKXmOWYRlcCiIF+kiBfF1xsfjG+L9I9hoaKlGAo/5kSN3dG++mWsbEunuksmrca6UAD7QG+eu883Ni11LSzGzOSIiUn2ejvu7FzIXmFWmvpQqCOJ3S10y7YAVYCs2EC2HQgHr4tTX71PccoZg9n3dHLcPyvHzXPrk+HnQrw2KbGcx9rceTf5t8UQkAqpOX39up3MBvPF+Cxob/xRdd6SW+Obmk3GuK0VnHHC9HzNmk4JHioiIdN662PZon2DB+riMyz3Yee6BOe7/aepr/yIfLzh+QGc6m8fU1NfmIo+fg2VNDCddHC8wgHT6fdiK1CVb4L9pjsd5I/V1mxy3Zyr19xCRLlAQX0d8U9OheL9XBE0d4uPxL0TQjtQQP3x4X+CiCJoaQDIZRTsiIlKf1sIKp30E/DjHMftjtVn+CbyCrRcPX+5KHZdrq7kgm2DnIvu0GJthbmLN/dG74qFU28eTPQAHGBq63ob9zn2xavBhx2J/u0we27t949QlYMsls3sQWypxONkD8/50nMW/LfX1R+SOL4bm+LmIlEjp9PUkFvse3kfTlvffxfY5lZ5i4MBj8L7zWRphzp3gR4w4x82cuSCS9kREpLvZFtsXHWz/ebC90S8NHXNejvs6rNAb5E73DlLpH8tx++PYNrn7YAFnZhG4x7Dq78Fa+v9gwe6jwP9ytHkb8L1U27djQe4c4OEcxxfjE2wP9klYfYJLsUEID4zCgujdSP8NwSrA749Vom/HiuftBFyObQmXLZD/F7a921+BX6TaP43cM/EfY8/P1dhWdxdiWxI6LPPhHGwZxpzU8fdhlfQPwP6Gk4Dp2HO+NXBSqp/fLPgXEZGCFMTXCT969GC8z7ZerHOc+6IfO7a/mzo180NRuivvD4uwrQZ69z6I9P61IiJSX+KsWW19cMbPcgXxhfTGglhIby2X6SNsq7ldsb3a7824fRE2S/8n4Fuhnx9C7iD+J9jvcDQWyIINBnQliAdbC98GXAZck3HbSuCBjJ89iwXgV2E7AwSuwpYSZCuQdyEWdO9BeuDjFez5uDlHv36P7V9/KWvuPz+NjgMjHjgK+C3wDdass7AAuC7H44hIiRTE14uGhnF4H93z7X1fVq0aCzwfWZtSbTtG3N5OKIgXEak19wNbYkFsLhcBVwLzQj87EAue3yvxcTpjZei+2aqwe2C71NfZedo5FKuynquS+3NYqvh6pGf+P8jT3nLgRCxIHY6ljS/Pcey5wMVYIcBi3IDNku+G/e4rscr2L5EuMhf2J2zmex+sYv1L2K4BG2HZC5nP73vYjPj+2Oz4DGAyNmv/HOk1/5muwwYKgn4txbb0m5Ll2OXY4MIFwO6px/kYeAubhW/P9cuLSGkUxNcL73Ots+q8hobo25Sq8OPHN7JgwZDIlluYYVE2JiIikVhC4W3hFqYuYXOyHdjFx8nFk78C/uoCtwcWpC6FfEzuQD+bNvIPHpTy2GHLgSdSl2K8D9yS8bN8gwZLgbuzPGahv+UybAnB40X264MsjyMiEVJhu/rRuwxtZltzN2wU+wAAAx1JREFUJd3R7NmNeN8QaZve6/UhIiIiIhIxBfH1wrno90htby82pU5qnJs9ewWlzUIU0ajT60NEREREJGIK4utFMtkadYs0NMyIuE2pJu+jfY14Pz3S9kREREREREF8vXCtrf8FogvSnHvRJRL5ir9IdxOLZVa/7Zpk8v5I2xMREREREQXxdSa6SuHJpKqO9zQNDbcCKyJq7Vk3Y4YyNUREREREIqYgvp4sW3Y18E4ELbWwwQY3RtCO1BA3deocnLsqgqY8sdg5EbQjIiIiIiIZFMTXETd79gqc+xq292gnG3HLce4kN3lyW3Q9k5rxyScXAC93sZVL3bRpz0XRHRERERER6UhBfJ1xicSzeH8qtsdqqVaQTB7nEomuBnlSo9ycOcuJxY4AZnaqAe//SkvLz6LtlYiIiIiIBBTE1yHX2nozzu0HzCvhbu8A411r671l6pbUCDdt2rs4txPwjxLuthrnzqe19TgHyXL1TURERESk3imIr1MukXgS50YAFwH5qszPwbkf8+mno11Ly4sV6p5UmUskPnItLfvj3MHAi4DPfqBbjnN3kEyOcYnEL1yu40REREREJBKN1e6AVI9LJJYAF3i4kKamHYnFxuL9RoDH+/doaHidadOmKDCrXy6ReBB40I8dO5y2tr3wfmOcGwjMIxabRSz2pJs6dWm1+ykiIiIiUi8UxAsOkrS2vgC8UO2+SG1yU6fOAW6tdj9EREREROqd0ulFREREREREugkF8SIiIiIiIiLdhIJ4ERERERERkW5CQbyIiIiIiIhIN6EgXkRERERERKSbUBAvIiIiIiIi0k0oiBcRERERERHpJrRPfDfnvV97woQJ6+c7pq2tbeUNN9ywOMrHPfvss/suXbq0f4G+rRflY0rpnHO9C70+AK699tqFgI/qcSdOnBibO3fuoELHee97RfWYIiIiIiL1QEF893djoQMaGxsfAA6J8kGXLFnyPefcJfmOcc5F+ZDSCd77/YAFhY771re+Neiaa675OKrHfeeddzZsbGx8r9Bxeo2IiIiIiJRG6fQiIiIiIiIi3cT/B1Sz+3D9oR4WAAAAAElFTkSuQmCC"
+ "fig-pdes-dot-product.png": {
+ "image/png": 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"
}
},
"cell_type": "markdown",
- "id": "f0260d7b",
+ "id": "a8aa2ebd",
"metadata": {},
"source": [
"
\n",
+ "Question: Which MPI directive best incorporates the communication of the local dot product among processes?\n",
+ "
\n",
+ "\n",
+ " a) MPI_Gather\n",
+ " b) MPI_Reduce\n",
+ " c) MPI_Allreduce\n",
+ " d) MPI_Bcast"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0907af2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "answer=\"x\" # Replace x with a, b, c, or d\n",
+ "pdes_check_2(answer)"
]
},
{
@@ -1112,7 +1268,8 @@
"id": "759aefd1",
"metadata": {},
"source": [
- "### Sparse matrix-vector product\n"
+ "### Sparse matrix-vector product\n",
+ "Next we will discuss how to parallelize the product of a sparse matrix $A$ with a vector $x$. "
]
},
{
@@ -1140,8 +1297,21 @@
"Question: Which parts of $A$ and $x$ are needed to compute the local values of $b$ in a worker? \n",
"
\n",
"\n",
- "\n",
- "- Answer: Only the entries of x associated with the non-zero columns of A stored in the worker."
+ " a) The local entries of A and all entries of x.\n",
+ " b) The local entries of A and the local entries of x. \n",
+ " c) All entries of A and the entries of x associated with local non-zero columns of A. \n",
+ " d) The local entries of A and the entries of x associated with the local non-zero columns of A. \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "53672399",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "answer=\"x\" # Replace x with a, b, c, or d\n",
+ "pdes_check_3(answer)"
]
},
{
@@ -1149,7 +1319,8 @@
"id": "4afca5ab",
"metadata": {},
"source": [
- "### Ghost (halo) columns"
+ "### Ghost (halo) columns\n",
+ "In our example, each CPU needs all local entries of $x$ plus two additional entries from another machine. If all entries in $A$ were non-zero, the whole vector $x$ would be required. Thus, the sparsity of $A$ allows to reduce the amount of communication. This pays off especially in larger problems. "
]
},
{
@@ -1174,7 +1345,11 @@
"source": [
"### Latency hiding\n",
"\n",
- "A = A_own + A_ghost\n"
+ "We can also use latency hiding for this problem. The computations are split into two parts: \n",
+ "1. Multiplication with only local values of $x$ \n",
+ "2. Multiplication with the remaining communicated values of $x$\n",
+ "\n",
+ "The first part of the computations has no data dependencies, so it can be started immediately. During its computation, the communication of the values of $x$ can be started. "
]
},
{
@@ -1203,31 +1378,50 @@
},
{
"cell_type": "markdown",
- "id": "a6433395",
+ "id": "149111fa",
"metadata": {},
"source": [
"
\n",
"Question: Which mesh partition does lead to less communication in the sparse matrix-vector product?\n",
- "
"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "5e2926d0",
+ "metadata": {},
+ "source": [
+ " a) The 2D block partitioning\n",
+ " b) The 2D cyclic partitioning\n",
+ " c) Both are equally good"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7f67a66a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "answer=\"x\" # Replace x with a, b, or c \n",
+ "pdes_check_4(answer)"
+ ]
+ },
{
"cell_type": "markdown",
"id": "a91edb5d",
@@ -1235,7 +1429,9 @@
"source": [
"Remember: The equation associated with point (i,j) is:\n",
"\n",
- "$u_{i-1,j} + u_{i+1,j} + u_{i,j-1} + u_{i,j+1} - 4u_{i,j} = 0$"
+ "$u_{i-1,j} + u_{i+1,j} + u_{i,j-1} + u_{i,j+1} - 4u_{i,j} = 0$\n",
+ "\n",
+ "Each equation for a point corresponds with a row in the matrix $A$. Each row will have only 5 non-zero values, and it reduces communication if they are on the same CPU. Therefore, the 2D block partitioning is more suitable, since only the boundary values need to be communicated."
]
},
{
@@ -1261,10 +1457,7 @@
"source": [
"## How to partition unstructured meshes?\n",
"\n",
- "- FEM methods work on unstructured meshes\n",
- "- One equation per node\n",
- "- Non-zero columns are associated with nodes connected by mesh edges\n",
- "\n"
+ "Finite element methods also work on unstructured meshes. The grid of points can be modeled as a graph. Again, there will be one equation per node in the graph. The partition can be computed as the result of a k-way graph partitioning problem.\n"
]
},
{
@@ -1290,10 +1483,10 @@
"### k-way graph partitioning problem\n",
"\n",
"\n",
- "Given a graph $G$ (i.e. the mesh)\n",
+ "Given a graph $G$ (i.e. the mesh),\n",
"\n",
- "- Partition the vertices of $G$ into k disjoint parts of equal size (load balance)\n",
- "- Minimize the number of edges with end vertices belonging to different parts (reduce communication)\n",
+ "- Partition the vertices of $G$ into $k$ disjoint parts of equal size (to achieve load balance)\n",
+ "- Minimize the number of edges with end vertices belonging to different parts (to reduce communication)\n",
"\n",
"\n",
"\n"
@@ -1311,18 +1504,37 @@
"source": [
"### Example\n",
"\n",
- "- Partition of a mesh into 8 parts\n",
- "- Computed with [METIS](https://github.com/KarypisLab/METIS)\n",
+ "The picture shows a partition of a mesh into 8 parts (computed with [METIS](https://github.com/KarypisLab/METIS)).\n",
"\n",
"
\n",
"\n",
"
\n"
]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b706b055",
+ "metadata": {},
+ "source": [
+ "# License\n",
+ "\n",
+ "\n",
+ "\n",
+ "This notebook is part of the course [Programming Large Scale Parallel Systems](https://www.francescverdugo.com/XM_40017) at Vrije Universiteit Amsterdam and may be used under a [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "87beee72",
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
"kernelspec": {
- "display_name": "Julia 1.9.0",
+ "display_name": "Julia 1.9.1",
"language": "julia",
"name": "julia-1.9"
},
@@ -1330,7 +1542,7 @@
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
- "version": "1.9.0"
+ "version": "1.9.1"
}
},
"nbformat": 4,
diff --git a/dev/pdes/index.html b/dev/pdes/index.html
index ea96bdc..ae0384c 100644
--- a/dev/pdes/index.html
+++ b/dev/pdes/index.html
@@ -1,5 +1,5 @@
-Partial differential equations · XM_40017
Given the temperature at the boundary of a room, predict the temperature at any point in the room. The illustration below shows the temperature at different intervals of the room boundary (including a window and a heater).
+
We will discuss the serial implementation first and learn how to compute it in parallel afterwards.
Main idea: Transform a PDE into a system of algebraic equations.
+
All of them follow the same main idea: to transform a PDE into a system of linear equations. The problem is first discretized into a computational mesh. The solution to the PDE at any point of the mesh is then given by the solution for vector x.
To solve the temperature distribution in the room, we choose the Finite Difference method. The advantage of this method is that it is easy to implement and computationally efficient. On the other hand, it is not suitable for more complex geometries.
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Goal: Compute the temperature at each grid point
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The (unknown) values can be stored in a computer using an array
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u[i,j] is the temperature at point $(i,j)$
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With the finite difference method, we first model the room as a grid, which can be stored using an array. Each entry of the matrix u[i,j] represents the temperature in the room at point $(i,j)$. The goal is now to compute the temperature at each grid point.
How to find the temperature at the interior points?¶
To set up a system of linear equations, we have to find one equation for each unknown value in the grid. The Laplace equation can be approximated by the following linear equation:
We can now obtain this kind of equation for every unknown point in the grid (= all the interior points). All these equations can be arranged in array form
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$$Ax=b.$$
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+Question: For a grid of size NxN, how many rows does matrix A have?
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a) N
+b) N²
+c) N-1
+d) (N-1)x(N-1)
+e) N-2
+f) (N-2)x(N-2)
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answer="x"# Replace x with a,b,c,d,e, or f
+pdes_check_0(answer)
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We created the matrix $A$ of the system of linear equations $Ax=b$ for a grid size $N=5$. For a grid of this size, we need 9 linear equations since there are 9 interior points. Note that we are using two types of numeration: one that defines the coordinates in the grid, e.g. $(2,3)$. The other numeration simply enumerates the interior values row-wise (1,2,...,9). The latter numeration is used to construct the matrix $A$.
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For any grid size, the equations matrix will only contain up to 5 nonzero values per row. Therefore, it is useful to store it as a sparse array which only stores the nonzero values under the hood. This is essential for the implementation because a lot of memory can be saved in this way.
A computation method is an algorithmically scalable solver if the total cost scales at most linearly with respect to the total problem size.
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+Question: Let $R$ be our problem size, the number of rows in our matrix $A$. Because we have $N$ points in each dimension of the grid, $R=O(N^2)$. What is the complexity of the Jacobi method and Gaussian elimination with respect to the problem size $R$?
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a) Jacobi: O(N^2) = O(R) per iteration, GE: O(N^3) = O(R*sqrt(R))
+b) Jacobi: O(N^2) = O(R) per iteration, GE: O(R^3)
+c) Jacobi: O(R^2) per iteration, GE: O(R^3)
+d) Jacobi: O(N^2) = O(R) per iteration, GE: O(R)
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answer="x"# Replace x with a,b,c or d
+pdes_check_1(answer)
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If the total cost scales at most linearly with respect to the total problem size
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-Question: Are Gaussian elimination and Jacobi algorithmically scalable?
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Answer: NO.
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Problem size is R = O(N^2)
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Gaussian Elimination: O(R^3) (more than linear)
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Jacobi: O(N^2) = O(R) per iteration. Linear per iteration, but how many iterations?
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To conclude, we find that none of the two solvers are algorithmically scalable. The Jacobi method scales linearly with respect to the problem size per iteration. However, the number of iterations might increase with the problem size, as we will demonstrate in the next section.
Next we implement the Jacobi method to solve the PDE. In addition to using a fixed maximum number of iterations, we introduce a relative tolerance threshold. The iterations stop when the relative error in an iteration subceeds this threshold.
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Run the following code cell several times with different values for $N$. For instance, $N=10$, $N=20$, $N=40$. Compare the number of iterations that are needed to converge for each problem size.
We can observe that the number of iterations scales with the problem size. In the next step, we plot the number of iterations needed for convergence against the problem size.
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The number of iterations to achieve a relative error of $10^{-s}$ increases as $O(s N^2)$
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Remember: The cost per iteration is $O(N^2)$
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Total cost is $O(N^4) = O((N^2)^2) $
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It can be analytically proven that the number of iterations to achieve a relative error of $10^{-s}$ increases as $O(s N^2)$. Recall: The cost per iteration of the Jacobi method is $O(N^2) = O(R)$. Treating $s$ as a constant, the total cost of the Jacobi method will be $O((N^2)^2) = O(R^2) = O(N^4)$.
There exist several methods to solve a Laplace equation on a regular mesh of $S$ points ($S=N^d$). The following table compares the scalability of these solvers for different numbers of mesh dimensions.
Idea: Transform the problem into an optimization problem
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$A x = b$
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equivalent to
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$ x = \text{arg }\min_{y} f(y)$ with $ f(y)= \frac{1}{2}( y^\mathrm{T}Ay - y^\mathrm{T} b )$
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We can use some sort of gradient descent to solve it
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The Conjugate Gradient Method is a gradient descent algorithm optimized for symmetic ($A^\mathrm{T}=A$) positive-definite ($y^\mathrm{T}Ay > 0$) matrices
In this section, we will discuss the Conjugate Gradient Method. Combined with the multi grid method, it achieves the best scalability for solving Laplace equations.
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The idea of the conjugate gradient method is to transform the problem into an optimization problem of the form
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$$ x = \text{arg }\min_{y} f(y)$$
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with
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$$ f(y)= \frac{1}{2}( y^\mathrm{T}Ay - y^\mathrm{T} b ).$$
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Thus, the goal is to find vector $x$, the minimum of function $f(y)$. This vector is equivalent to the solution of the system of linear equations, $A x = b$.
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The conjugate gradient method is a gradient descent algorithm optimized for symmetic ($A^\mathrm{T}=A$) positive-definite ($y^\mathrm{T}Ay > 0$) matrices. The algorithm is applicable to our problem since the matrix $A$ is both symmetric and positive-definite.
The conjugate gradient method is a type of Krylov subspace method, which is listed by the IEEE Computer Society as one of the top 10 algorithms of the past century:
The number of iterations needed to converge is much lower with the conjugate gradient method than with the Jacobi method. However, the number of iterations still increases with the problem size, so there is still room for improvement.
It can be shown that the number of iterations to achieve a relative error of $10^{-s}$ increases as $O(s \sqrt{\kappa(A)})$. As a reminder, the condition number of A $\kappa(A)$ is ratio between the largest and smallest eigenvalues of A:
+$$\kappa(A)=\dfrac{\lambda_{max}(A)}{\lambda_{min}(A)}$$
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It can also be shown that in our example $\kappa(A) = O(N^2)$. Thus, the number of iterations scales as $O(s N)$ for the conjugate gradient method. Remember that the number of iterations scales as $O(sN^2)$ for the Jacobi method.
The conjugate gradient method provides some improvement to the Jacobi method, since the number of iterations increases more slowly with the problem size. However, the ultimate goal is to find an iterative method whose number of iterations is independent of the problem size.
To achieve this goal, we will apply a preconditioner to transform the original problem into a format that is easier to solve.
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A preconditioner is simply a linear function $M$ such that
+$$M(b) \approx x, \text{ with } Ax=b \text{ for any } b.$$
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Once we have found such a function $M(b)$, we can solve the preconditioned problem $(MA)x = Mb$, which is equivalent to $Ax=b$.
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The way that preconditioning affects the number of iterations is that it reduces the condition number of $A$:
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$$ \begin{align}
+&M(b) \approx x, \text{ with } Ax=b \text{ for any } b \\
+&\rightarrow M(b) \approx A^{-1}b \\
+&\rightarrow M \approx A^{-1} \\
+&\rightarrow M A \approx I \text{ (Identity matrix)} \\
+&\rightarrow \kappa (MA) \approx 1 \\
+\end{align}$$
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Recall that the number of iterations to achieve a relative error of $10^{-s}$ is $O(s\sqrt{\kappa (A)})$. With $\kappa (MA) \approx 1$, the number of iterations will scale as $O(s)$. Thus, the number of iterations will be independent of the problem size for the preconditioned problem.
Many ways of building a preconditioner can be found in the literature. The solutions range between two extremes:
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$M=A^{-1}$. This is the exact preconditioner. The CG method will converge in just one iteration. However, it is exactly as costly to compute the matrix $M$ as it is to find the solution to the original problem.
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$M=I$. This is the other extreme where we do no extra work to find the matrix $M$. The preconditioned problem is identical to the original problem.
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The solution is to find a good trade-off such that we get a matrix $M$ that is accurate enough to approximate the original problem but also cheap enough to be computed quickly.
Next we use the Jacobi method to construct a preconditioner for the CG method. The matrix $M$ is simply computed as the grid values after a certain number of iterations with the Jacobi method. In order for $M$ to be linear, we set a fixed maximum number of iterations in the jacobi! function and disable the relative error stopping criterion.
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Run the following cell for different values of niters. The larger the value of niters, the more matrix $M$ resembles the exact solution. The fewer iterations we use, the less accurate the approximation.
Observe that the Jacobi preconditioner achieves that the CG method needs fewer iterations. However, the number of iterations still increases with the problem size.
We can optimize the Jacobi method to obtain a better preconditioner for the CG method. Run the following code cell again with a grid size of $N=10$ and $N=100$.
The main reason that the preconditioner performs badly for larger problem sizes is that it takes more time to update the interior values of the grid. The larger the grid, the longer the Jacobi method needs to propagate the values from the outer boundary to the interior of the grid.
The multi-grid method is the second building block of the fast solver for our Laplace equation.
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The multi-grid method achieves a faster convergence of the Jacobi method by starting to solve the coarse problem first and then stepwise increasing the grid resolution. On each resolution level, a few steps of the Jacobi method are performed (only 2 steps per level in this example). Next, the resolution of the grid is increased and the Jacobi method is run again. This process is repeated until we reach the desired grid size. Since the Jacobi method converges after a few iterations on very small grids, the values are propagated to the center much more quickly than without using the multi-grid method.
Let's see what happens when we use the CG method in combination with a multi-grid preconditioner. In the following code cell, we use a multi-grid preconditioner from the package Preconditioners.
The number of iterations to achieve a relative error of $10^{-s}$ increases as $O(s)$
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The cost of each iteration is proportional to the number of grid points
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The number of iterations is now constant for different problem sizes. Thus, we have finally achieved an algorithmically scalable solver by combining the conjugate gradient method and the multi-grid method!
The HPCG benchmark is an alternative to the HPL benchmark to rank the top 500 computers. It is based on the CG + multigrid method, whereas the HPL benchmark is a Gaussian elimination type of algorithm. The HPCG was introduced because the CG + multigrid method is more commonly run on supercomputers than Gaussian elimination. While HPL gives a good indication of the peak performance, the HPCG benchmark is orders of magnitude below the peak performance.
For the parallel implementation, the processes can compute the dot product of their respective sub-vectors independently. But they have to send the result to the other processes so they can be combined for the final dot product. Each process needs the global dot product, because further computations in the algorithm depend on it. For instance, the stopping criterion depends on the result of the global dot product, such that all processes stop at the same time.
Next we will discuss how to parallelize the product of a sparse matrix $A$ with a vector $x$.
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Question: Which parts of $A$ and $x$ are needed to compute the local values of $b$ in a worker?
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Answer: Only the entries of x associated with the non-zero columns of A stored in the worker.
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a) The local entries of A and all entries of x.
+b) The local entries of A and the local entries of x.
+c) All entries of A and the entries of x associated with local non-zero columns of A.
+d) The local entries of A and the entries of x associated with the local non-zero columns of A.
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answer="x"# Replace x with a, b, c, or d
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In our example, each CPU needs all local entries of $x$ plus two additional entries from another machine. If all entries in $A$ were non-zero, the whole vector $x$ would be required. Thus, the sparsity of $A$ allows to reduce the amount of communication. This pays off especially in larger problems.
We can also use latency hiding for this problem. The computations are split into two parts:
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Multiplication with only local values of $x$
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Multiplication with the remaining communicated values of $x$
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The first part of the computations has no data dependencies, so it can be started immediately. During its computation, the communication of the values of $x$ can be started.
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Question: Which mesh partition does lead to less communication in the sparse matrix-vector product?
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Answer: 2d block (as for Jacobi method)
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a) The 2D block partitioning
+b) The 2D cyclic partitioning
+c) Both are equally good
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answer="x"# Replace x with a, b, or c
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Remember: The equation associated with point (i,j) is:
Each equation for a point corresponds with a row in the matrix $A$. Each row will have only 5 non-zero values, and it reduces communication if they are on the same CPU. Therefore, the 2D block partitioning is more suitable, since only the boundary values need to be communicated.
Finite element methods also work on unstructured meshes. The grid of points can be modeled as a graph. Again, there will be one equation per node in the graph. The partition can be computed as the result of a k-way graph partitioning problem.