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Merge pull request #9 from fverdugo/francesc

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Adding exercises to MPI tutorial
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Francesc Verdugo authored and GitHub committed 2023-08-29 11:20:38 +02:00
2 parents 98ee1a2e08 0b53e10b89
commit ed7bcf8c06
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docs/make.jl
+1 -1
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@@ -119,7 +119,7 @@ makedocs(;
"Julia Basics" => "julia_basics.md",
"Tasks and channels" => "julia_async.md",
"Remote calls and remote channels" => "julia_distributed.md",
# "MPI" => "mpi_tutorial.md",
"MPI" => "mpi_tutorial.md",
"Matrix Multiplication"=>"matrix_matrix.md",
# "Jacobi" => "jacobi_method.md",
# "ASP" => "asp.md",
notebooks/figures/fig_mpi.svg
+278 -9
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notebooks/mpi_tutorial.ipynb
+139 -19
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@@ -43,9 +43,9 @@
"## 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 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 above 1k pages (see https://www.mpi-forum.org/docs/mpi-4.0/mpi40-report.pdf)\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"
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"### Hello-world example"
"### Installing MPI in Julia\n",
"\n",
"MPI can be installed as any other Julia package using the package manager."
]
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@@ -69,6 +71,24 @@
"] add MPI"
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"<div class=\"alert alert-block alert-info\">\n",
"<b>Note:</b> 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 <a href=\"https://github.com/JuliaParallel/MPI.jl\">https://github.com/JuliaParallel/MPI.jl</a>\n",
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"### Hello world\n",
"\n",
"Julia code typically needs to be in a file to run in with MPI. Let's us write our hello world in a file:"
"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:"
]
},
{
@@ -258,6 +278,16 @@
"end"
]
},
{
"cell_type": "markdown",
"id": "cb480322",
"metadata": {},
"source": [
"<div class=\"alert alert-block alert-info\">\n",
"<b>Note:</b> Note that the rank ids start with 0.\n",
"</div>"
]
},
{
"cell_type": "markdown",
"id": "6caa8d74",
@@ -265,7 +295,7 @@
"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 identifier assigned to each process within a communicator. It allows processes to distinguish and address each other in communication operations.\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",
@@ -296,7 +326,7 @@
"source": [
"### Scatter\n",
"\n",
"TODO explain what this code does"
"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."
]
},
{
@@ -345,7 +375,7 @@
"source": [
"### Gather\n",
"\n",
"TODO explain what this code does"
"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)."
]
},
{
@@ -394,11 +424,14 @@
"\n",
"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:\n",
"\n",
"- `MPI.Isend`, and `MPI.Irecv!`\n",
"- `MPI.Isend`, and `MPI.Irecv!` (*non-blocking directives*)\n",
"- `MPI.Send`, and `MPI.Recv` (*blocking directives*)\n",
"\n",
"which are *non-blocking directives*.\n",
"\n",
"MPI also offers *blocking* directives with different blocking behaviors (communication modes). Blocking communication will be discussed later in the course.\n",
"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.\n",
"\n",
"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. \n",
"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.\n",
"\n"
]
},
@@ -479,23 +512,110 @@
},
{
"cell_type": "markdown",
"id": "5e8f6e6a",
"id": "9dc6c3fb",
"metadata": {},
"source": [
"# License\n",
"\n",
"TODO: replace link to website\n",
"\n",
"This notebook is part of the course [Programming Large Scale Parallel Systems](http://localhost:8000/) at Vrije Universiteit Amsterdam and may be used under a [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license."
"### Example (with blocking directives)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c9364808",
"id": "a046d7dd",
"metadata": {},
"outputs": [],
"source": []
"source": [
"@everywhere workers() begin\n",
" comm = MPI.Comm_dup(MPI.COMM_WORLD)\n",
" rank = MPI.Comm_rank(comm)\n",
" nranks = MPI.Comm_size(comm)\n",
" snder = 0\n",
" rcver = nranks-1\n",
" buffer = Ref(0)\n",
" if rank == snder\n",
" msg = 10*(rank+2)\n",
" println(\"I am sending: $msg\")\n",
" buffer[] = msg\n",
" MPI.Send(buffer,comm;dest=rcver,tag=0)\n",
" MPI.Recv!(buffer,comm,source=rcver,tag=0)\n",
" msg = buffer[]\n",
" println(\"I have received: $msg\")\n",
" end\n",
" if rank == rcver\n",
" MPI.Recv!(buffer,comm,source=snder,tag=0)\n",
" msg = buffer[]\n",
" println(\"I have received: $msg\")\n",
" coef = (rank+2)\n",
" msg = msg*coef\n",
" println(\"I am sending: $msg\")\n",
" buffer[] = msg\n",
" MPI.Send(buffer,comm;dest=snder,tag=0)\n",
" end\n",
"end"
]
},
{
"cell_type": "markdown",
"id": "d9bdfa02",
"metadata": {},
"source": [
"<div class=\"alert alert-block alert-warning\">\n",
"<b>Important:</b> 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",
"</div>"
]
},
{
"cell_type": "markdown",
"id": "d02f935d",
"metadata": {},
"source": [
"## Exercises"
]
},
{
"cell_type": "markdown",
"id": "a8e1c623",
"metadata": {},
"source": [
"### Exercise 1\n",
"\n",
"Implement this simple algorithm: 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.\n"
]
},
{
"attachments": {
"g5148.png": {
"image/png": 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truncated
}
},
"cell_type": "markdown",
"id": "d474d781",
"metadata": {},
"source": [
"<div>\n",
"<img src=\"attachment:g5148.png\" align=\"left\" width=\"320\"/>\n",
"</div>"
]
},
{
"cell_type": "markdown",
"id": "49adf214",
"metadata": {},
"source": [
"### Exercise 2\n",
"\n",
"Implement the same algorithm as in Exercise 1, but now without using MPI. Implement the communications using the native `Distributed` module provided by Julia. In this case, start using process 1 instead of rank 0."
]
},
{
"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": {
notebooks/solutions.ipynb
+92 -2
View File
@@ -291,6 +291,96 @@
"rmprocs(workers())"
]
},
{
"cell_type": "markdown",
"id": "508125f9",
"metadata": {},
"source": [
"### New ring example"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7af4cfd0",
"metadata": {},
"outputs": [],
"source": [
"@everywhere workers() begin\n",
" using MPI\n",
" MPI.Init()\n",
" comm = MPI.Comm_dup(MPI.COMM_WORLD)\n",
" rank = MPI.Comm_rank(comm)\n",
" nranks = MPI.Comm_size(comm)\n",
" buffer = Ref(0)\n",
" if rank == 0\n",
" msg = rand(1:10)\n",
" buffer[] = msg\n",
" println(\"msg = $(buffer[])\")\n",
" MPI.Send(buffer,comm;dest=rank+1,tag=0)\n",
" MPI.Recv!(buffer,comm;source=nranks-1,tag=0)\n",
" println(\"msg = $(buffer[])\")\n",
" else\n",
" dest = (rank != nranks-1) ? rank+1 : 0\n",
" MPI.Recv!(buffer,comm;source=rank-1,tag=0)\n",
" buffer[] += 1\n",
" println(\"msg = $(buffer[])\")\n",
" MPI.Send(buffer,comm;dest,tag=0)\n",
" end\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "30ece423",
"metadata": {},
"outputs": [],
"source": [
"ftrs = Vector{Future}(undef,nprocs())\n",
"for p in 1:nprocs()\n",
" ftrs[p] = @spawnat p RemoteChannel(()->Channel{Int}(1))\n",
"end\n",
"for p in 1:nprocs()\n",
" @spawnat p begin \n",
" chnl_snd = fetch(ftrs[p])\n",
" source = (p != 1) ? p-1 : nprocs()\n",
" chnl_rcv = fetch(ftrs[source])\n",
" if p == 1\n",
" msg = rand(1:10)\n",
" @show msg\n",
" put!(chnl_snd,msg)\n",
" msg = take!(chnl_rcv)\n",
" @show msg\n",
" else\n",
" msg = take!(chnl_rcv)\n",
" msg += 1\n",
" @show msg\n",
" put!(chnl_snd,msg)\n",
" end\n",
" end\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "761ce452",
"metadata": {},
"outputs": [],
"source": [
"@everywhere function work(msg)\n",
" msg += 1\n",
" println(\"msg = $msg\")\n",
" if myid() == nprocs()\n",
" @spawnat 1 println(\"msg = $msg\")\n",
" else\n",
" next = myid() + 1\n",
" @spawnat next work(msg)\n",
" end\n",
"end"
]
},
{
"cell_type": "markdown",
"id": "19641daf",
@@ -733,7 +823,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Julia 1.9.1",
"display_name": "Julia 1.9.0",
"language": "julia",
"name": "julia-1.9"
},
@@ -741,7 +831,7 @@
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.9.1"
"version": "1.9.0"
}
},
"nbformat": 4,