@time compute_π(100_000_000)
+t = @elapsed compute_π(100_000_000)
a) 10*t
+b) t
+c) 0.1*t
+d) near 0*t
+a) 10*t
+b) t
+c) 0.1*t
+d) near 0*t
+
+a) 10*t
+b) t
+c) 0.1*t
+d) near 0*t
+a) infinity
+b) 1 second
+c) near 0 seconds
+d) 3 seconds
+a) infinity
+b) 1 second
+c) near 0 seconds
+d) 3 seconds
+Which will be the value of x in the last line ?
Once defined, a function can be called using bracket notation.
+Once defined, a function can be called using bracket notation as you would expect.
result = add(1,3)
+add(1,3)
function q(x)
+ x = 2
+ x
+end
+x = 1
+y = q(x)
+x
+Which will be the value of x below?
using Printf
-
-function answer_checker(answer,solution)
- if answer == solution
- "🥳 Well done! "
- else
- "It's not correct. Keep trying! 💪"
- end |> println
-end
-q_1_check(answer) = answer_checker(answer,"a")
-q_2_check(answer) = answer_checker(answer,"b")
-using Printf
+function answer_checker(answer,solution)
+ if answer == solution
+ "🥳 Well done! "
+ else
+ "It's not correct. Keep trying! 💪"
+ end |> println
+end
+q_1_check(answer) = answer_checker(answer,"a")
+q_2_check(answer) = answer_checker(answer,"b")
+How many integers are transferred between master and worker? Including both directions.
+a = rand(Int,4,4)
proc = 4
@fetchfrom proc sum(a^2)
@@ -15663,7 +15676,9 @@ d) 65
How many integers are transferred between master and worker? Including both directions.
+a = rand(Int,4,4)
proc = 4
@fetchfrom proc sum(a[2,2]^2)
@@ -15701,7 +15716,9 @@ d) 32
Which value will be the value of x ?
Which value will be the value of x ?
Which value will be the value of x ?
-We will learn
+With this notebook, you will learn
We are going to use Jupyter notebooks in this and other lectures. You provably have worked with notebooks (in Python). If not, here are the basic concepts you need to know to follow the lessons.
+To run a Julia Jupyther notebook, open a Julia REPL and type
+julia> ]
+pkg> add IJulia
+julia> using IJulia
+julia> notebook()
+A new browser window will open. Navigate to the corresponding notebook and open it.
+As you can see from the output of previous cell, the value of the last line is displayed. We can suppress the output with a semicolon. Try it. Execute next cell.
+ +1+3
+4*5;
+Running the two cells below in reverse order won't work (try it).
+] add BenchmarkTools DataFrames PyCall Conda Test
+] add MPI
; ls
+NB. Most of the examples below are taken from the lecture by S.G. Johnson at MIT. See here: +https://github.com/mitmath/18S096/blob/master/lectures/lecture1/Boxes-and-registers.ipynb
+# ✍️ Exercise 1
-function sum_hand(a)
- # TODO
+function sum_hand(a)
+ s = zero(eltype(a))
+ for ai in a
+ s += ai
+ end
+ s
end
@@ -14828,7 +14888,7 @@ body[data-format='mobile'] .jp-OutputArea-child .jp-OutputArea-output {
In order to track the performance of your code, it is useful to time the execution of single functions. In Julia, the most conventional way of measuring the computation time is the macro @time.
In Julia, the most straight-forward way of measuring the computation time of a piece of code is with the macro @time.
Note that @time also measures the compile time of a function if it's the first call to that function. So make sure to run @time twice on a freshly compiled function in order to get a more meaningful result.
Now in order to benchmark our code, we need to run it several times. To do this we can call our code in a for-loop and gather the runtimes using the Julia macro @elapsed. This measures the runtime of an expression in seconds, just as the @time macro, only @elapsed discards the result of the computation and returns the elapsed time instead.
A part of getting rid of compilation time, one typically wants to measure the runtime several times and compute sole. To do this we can call our code in a for-loop and gather the runtimes using the Julia macro @elapsed. This measures the runtime of an expression in seconds, just as the @time macro, only @elapsed discards the result of the computation and returns the elapsed time instead.
+In this notebook, we will:
+In this notebook, we will:
-Implement algorithm 3 in the function below. For simplicity, assume that the number of rows of C is a multiple of the number of workers.
+Implement algorithm 3 in the function below. For simplicity, assume that the number of rows of C is a multiple of the number of workers.
![](\"https://upload.wikimedia.org/wikipedia/commons/thumb/3/39/VU_logo.png/800px-VU_logo.png?20161029201021\"){kind=logo}
\n",
"\n",
"### Programming large-scale parallel systems\n",
- "### Lectures on Julia for HPC\n",
"\n",
"\n",
- "# Asynchronous programming in Julia\n",
- "\n",
- "by Francesc Verdugo (VU Amsterdam)\n",
- "\n",
- "Version fall 2022"
+ "# Asynchronous programming in Julia\n"
]
},
{
@@ -583,14 +578,10 @@
},
{
"cell_type": "markdown",
- "id": "2a23b1c3",
+ "id": "81de1c86",
"metadata": {},
"source": [
- "## Questions\n",
- "\n",
- "How long the next cells will take each?\n",
- "\n",
- "### Q1 (Asynchronous.ipynb)"
+ "## Questions"
]
},
{
@@ -600,7 +591,22 @@
"metadata": {},
"outputs": [],
"source": [
- "@time compute_π(100_000_000)"
+ "t = @elapsed compute_π(100_000_000)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2a23b1c3",
+ "metadata": {},
+ "source": [
+ "\n",
"\n",
"### Programming large-scale parallel systems\n",
- "### Lectures on Julia for HPC\n",
"\n",
"\n",
- "# Basics of Julia\n",
- "\n",
- "by Francesc Verdugo (VU Amsterdam)\n",
- "\n",
- "Version fall 2022"
+ "# Julia basics"
]
},
{
@@ -437,9 +432,11 @@
"id": "d36ba0ae",
"metadata": {},
"source": [
- "### Question 1\n",
"\n",
- "Which will be the value of `x` in the last line ?"
+ "\n",
"\n",
- "### Programming large-scale parallel systems\n",
- "### Lectures on Julia for HPC"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "aebf6482",
- "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",
- "q_1_check(answer) = answer_checker(answer,\"a\")\n",
- "q_2_check(answer) = answer_checker(answer,\"b\")"
+ "### Programming large-scale parallel systems\n"
]
},
{
@@ -36,11 +15,7 @@
"id": "2133c064",
"metadata": {},
"source": [
- "# Distributed computing in Julia\n",
- "\n",
- "by Francesc Verdugo (VU Amsterdam)\n",
- "\n",
- "Version fall 2022"
+ "# Distributed computing in Julia\n"
]
},
{
@@ -59,6 +34,35 @@
"With this knowledge you will be able to implement simple and complex parallel algorithms in Julia."
]
},
+ {
+ "cell_type": "markdown",
+ "id": "b9a04db7",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Programming large-scale parallel systems"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "038e5442",
+ "metadata": {},
+ "source": [
+ "# Matrix-matrix multiplication"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f70e2f35",
+ "metadata": {},
+ "source": [
+ "## Contents\n",
+ "\n",
+ "In this notebook, we will:\n",
+ "\n",
+ "- Parallelize a simple algorithm\n",
+ "- Study the performance of different parallelization strategies\n",
+ "- Implement them using Julia"
+ ]
+ },
{
"cell_type": "markdown",
"id": "480af594",
@@ -49,28 +81,6 @@
"alg_3_complex_check(answer) = answer_checker(answer, \"d\")"
]
},
- {
- "cell_type": "markdown",
- "id": "038e5442",
- "metadata": {},
- "source": [
- "# Distributed matrix-matrix multiplication"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f70e2f35",
- "metadata": {},
- "source": [
- "## Contents\n",
- "\n",
- "In this notebook, we will:\n",
- "\n",
- "- Parallelize a simple algorithm\n",
- "- Study the performance of different parallelization strategies\n",
- "- Implement them using Julia"
- ]
- },
{
"cell_type": "markdown",
"id": "96d2693d",
@@ -82,7 +92,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"id": "88bc2633",
"metadata": {},
@@ -118,7 +127,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"id": "5828e243",
"metadata": {},
@@ -247,7 +255,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"id": "6a706283",
"metadata": {},
@@ -278,7 +285,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"id": "acfb354b",
"metadata": {},
@@ -329,7 +335,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"id": "70087bce",
"metadata": {},
@@ -556,7 +561,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"id": "62e5c637",
"metadata": {},
@@ -616,7 +620,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"id": "fb6b572b",
"metadata": {},
@@ -775,7 +778,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"id": "f1b8c712",
"metadata": {},
@@ -835,7 +837,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"id": "c14ebcb3",
"metadata": {},
@@ -927,7 +928,7 @@
"id": "a628a1df",
"metadata": {},
"source": [
- "### Implementation of algorithm 3\n",
+ "### Exercise 1\n",
"\n",
"Implement algorithm 3 in the function below. For simplicity, assume that the number of rows of C is a multiple of the number of workers.\n"
]
@@ -1012,7 +1013,7 @@
"id": "fa8d7f40",
"metadata": {},
"source": [
- "### A more practical version of algorithm 1"
+ "### Exercise 2"
]
},
{
@@ -1119,7 +1120,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Julia 1.9.1",
+ "display_name": "Julia 1.9.0",
"language": "julia",
"name": "julia-1.9"
},
@@ -1127,7 +1128,7 @@
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
- "version": "1.9.1"
+ "version": "1.9.0"
}
},
"nbformat": 4,