mirror of
https://github.com/fverdugo/XM_40017.git
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1643 lines
422 KiB
Plaintext
1643 lines
422 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "edd3cb69",
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"metadata": {},
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"source": [
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"<div class=\"alert alert-block alert-info\">\n",
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"<b>Note:</b> Do not forget to execute the cells below before starting this notebook!\n",
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"</div>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "36579ad9",
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"metadata": {},
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"outputs": [],
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"source": [
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"function answer_checker(answer,solution)\n",
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" if answer == solution\n",
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" \"🥳 Well done! \"\n",
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" else\n",
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" \"It's not correct. Keep trying! 💪\"\n",
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" end |> println\n",
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"end\n",
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"alg_seq_check(answer) = answer_checker(answer,\"c\")\n",
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"alg_seq_loops_check(answer) = answer_checker(answer,\"d\")\n",
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"alg_1_deps_check(answer) = answer_checker(answer,\"b\")\n",
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"alg_1_complex_check(answer) = answer_checker(answer,\"d\")\n",
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"alg_1_time_check(answer) = answer_checker(answer,\"a\")\n",
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"alg_1_v2_complex_check(answer) = answer_checker(answer,\"c\")\n",
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"alg_1_v2_time_check(answer) = answer_checker(answer,\"a\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "038e5442",
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"metadata": {},
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"source": [
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"# Distributed matrix-matrix multiplication"
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]
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},
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{
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"cell_type": "markdown",
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"id": "96d2693d",
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"metadata": {},
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"source": [
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"## Problem Statement\n",
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"\n",
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"Let us consider the (dense) matrix-matrix product `C=A*B`:"
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]
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},
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{
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"attachments": {
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"fig_matmul_intro_0.png": {
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"image/png": 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"
|
|
}
|
|
},
|
|
"cell_type": "markdown",
|
|
"id": "88bc2633",
|
|
"metadata": {},
|
|
"source": [
|
|
" <div>\n",
|
|
"<img src=\"attachment:fig_matmul_intro_0.png\" align=\"left\" width=\"450\"/>\n",
|
|
"</div>\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "495ef679",
|
|
"metadata": {},
|
|
"source": [
|
|
"Our goal is to compute the product in parallel using more than one process (distributed implementation).\n",
|
|
"\n",
|
|
"We assume that:\n",
|
|
"\n",
|
|
"- All matrices `A`,`B`, and `C` are initially stored in the master process\n",
|
|
"- The result will be overwritten in `C`"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {
|
|
"fig_matmul_machines.png": {
|
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"image/png": 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"
|
|
}
|
|
},
|
|
"cell_type": "markdown",
|
|
"id": "5828e243",
|
|
"metadata": {},
|
|
"source": [
|
|
" <div>\n",
|
|
"<img src=\"attachment:fig_matmul_machines.png\" align=\"left\" width=\"450\"/>\n",
|
|
"</div>\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "ca56a7fe",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Serial implementation\n",
|
|
"\n",
|
|
"Consider the (naive) sequential algorithm:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "af8dfb37",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"function matmul_seq!(C,A,B)\n",
|
|
" m = size(A,1)\n",
|
|
" n = size(A,2)\n",
|
|
" l = size(B,2)\n",
|
|
" z = zero(eltype(C))\n",
|
|
" for j in 1:l\n",
|
|
" for i in 1:m\n",
|
|
" C[i,j] = z\n",
|
|
" for k in 1:n\n",
|
|
" @inbounds C[i,j] += A[i,k]*B[k,j]\n",
|
|
" end\n",
|
|
" end\n",
|
|
" end\n",
|
|
" C\n",
|
|
"end"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "f967d2ea",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div class=\"alert alert-block alert-info\">\n",
|
|
"<b>Note:</b> 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",
|
|
"</div>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "5d2c98d5",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div class=\"alert alert-block alert-success\">\n",
|
|
"<b>Question:</b> Which is the computational complexity of the sequential algorithm above when multiplying square matrices of size N by N ? \n",
|
|
"</div>\n",
|
|
"\n",
|
|
" a) O(N)\n",
|
|
" b) O(N^2)\n",
|
|
" c) O(N^3)\n",
|
|
" d) O(log(N)*N)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "e219a674",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# replace x with a, b, c, or, d;\n",
|
|
"# and run the cell to check you answer\n",
|
|
"answer = \"c\" \n",
|
|
"alg_seq_check(answer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "0c2aeebd",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div class=\"alert alert-block alert-success\">\n",
|
|
"<b>Question:</b> The serial implementation above is written using three nested loops. Which are the ones that are trivially parallelizable? In other words, which are the loops that contain completely independent operations at each loop iteration and whose order can be changed without affecting the result?\n",
|
|
"</div>\n",
|
|
"\n",
|
|
" a) loop over k\n",
|
|
" b) loops over i and k\n",
|
|
" c) all loops\n",
|
|
" d) loops over i and j"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "b7ad3f54",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"answer = \"d\"\n",
|
|
"alg_seq_loops_check(answer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "48b73bf6",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Parallel algorithms\n",
|
|
"\n",
|
|
"We study three different parallel algorithms. For simplicity, we assume that matrices A, B, and C are N by N square matrices.\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d04c4659",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Parallel algorithm 1\n",
|
|
"\n",
|
|
"Each worker computes one entry of `C`. We need `P=N^2` workers."
|
|
]
|
|
},
|
|
{
|
|
"attachments": {
|
|
"fig_matmul_intro_q_1.png": {
|
|
"image/png": 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"
|
|
}
|
|
},
|
|
"cell_type": "markdown",
|
|
"id": "639dffce",
|
|
"metadata": {},
|
|
"source": [
|
|
" <div>\n",
|
|
"<img src=\"attachment:fig_matmul_intro_q_1.png\" align=\"left\" width=\"450\"/>\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "b5973043",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Data dependencies"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "a69b4b9e",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div class=\"alert alert-block alert-success\">\n",
|
|
"<b>Question:</b> Which are the data dependencies of the computations done by the worker in charge of computing entry C[i,j] ? \n",
|
|
"</div>\n",
|
|
"\n",
|
|
" a) column A[:,i] and row B[j,:]\n",
|
|
" b) row A[i,:] and column B[:,j]\n",
|
|
" c) the whole matrices A and B\n",
|
|
" d) row A[i,:] and the whole matrix B"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "dbf21585",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"answer = \"x\"\n",
|
|
"alg_1_deps_check(answer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "9126a56b",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Complexity"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "56a7e26e",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div class=\"alert alert-block alert-success\">\n",
|
|
"<b>Question:</b> Which is the complexity of the communication and computations done by a worker?\n",
|
|
"</div>\n",
|
|
"\n",
|
|
" a) O(N) communication and O(N^2) computation\n",
|
|
" b) O(N^2) communication and O(N) computation\n",
|
|
" c) O(N^3) communication and O(N) computation\n",
|
|
" d) O(N) communication and O(N) computation"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "c068b2c2",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"answer = \"x\"\n",
|
|
"alg_1_complex_check(answer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e16d6ee8",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Parallel efficiency\n",
|
|
"\n",
|
|
"The *speedup* represents how faster a parallel algorithm runs with respect to the serial one\n",
|
|
"\n",
|
|
"$$\\text{speedup} = \\dfrac{\\text{time serial algorithm}}{\\text{time parallel algorithm}}$$\n",
|
|
"\n",
|
|
"If we run an optimal parallel algorithm with P processes we expect it to run P times faster than the sequential implementation. I.e., the *optimal* speedup of a parallel algorithm on P processes is equal to P,\n",
|
|
"\n",
|
|
"$$\\text{optimal speedup} = P.$$\n",
|
|
"\n",
|
|
"However, the *observed* speedup would be lower in practice. The closer the observed speedup is to the optimal one, the more efficient will be the parallel algorithm. To quantify how close (or how far) a parallel algorithm is from an optimal one, the parallel efficiency is defined:\n",
|
|
"\n",
|
|
"$$\\text{efficiency} = \\dfrac{\\text{speedup}}{\\text{optimal speedup}} = \\dfrac{\\text{speedup}}{P}.$$\n",
|
|
"\n",
|
|
"\n",
|
|
"An optimal parallel algorithm will have efficiency equal to 1. A real parallel algorithm will usually have efficiency less than 1.\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "5bdb9e58",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Efficiency of algorithm 1\n",
|
|
"\n",
|
|
"To determine the (theoretical) efficiency of algorithm 1, we need to estimate the time of the serial algorithm and the time of the parallel one. Remember that when we say that an algorithm has complexity O(X), this is equivalent to say that the cost (e.g. the time) of running the algorithm is proportional to X (for X large enough). In other words, the time of the algorithm can be written as C*X for a suitable constant C.\n",
|
|
"\n",
|
|
"Using the computational complexities of the sequential and parallel algorithm 1 we can model the run times as follows\n",
|
|
"\n",
|
|
"- The time of the sequential algorithm is Cm*N^3\n",
|
|
"- The time of the parallel algorithm in each worker is (Cn + Cw)*N\n",
|
|
"\n",
|
|
"where\n",
|
|
"\n",
|
|
"- Cn is a constant related the network throughput (the smaller Cn the faster the network).\n",
|
|
"- Cm and Cw are constants related with the FLOPS in the master and workers respectively (the smaller Cm and Cw the faster the computations)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "961fb287",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div class=\"alert alert-block alert-success\">\n",
|
|
"<b>Question:</b> For which values of Cn, Cm, Cm the parallel algorithm 1 achieves the optimal efficiency? Assume that the time of the parallel algorithm is mainly the time spent in the workers. Since all the workers run in parallel, the time of the parallel algorithm can be approximated as the time in a worker.\n",
|
|
"</div>\n",
|
|
"\n",
|
|
" a) Cm == Cn + Cw\n",
|
|
" b) Cm*N^2 == Cn + Cw\n",
|
|
" c) Cm == (Cn + Cw)*N\n",
|
|
" d) Cm == (Cn + Cw)*P\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "f8527cba",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"answer = \"x\"\n",
|
|
"alg_1_time_check(answer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "fd92d631",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Implementation of algorithm 1"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "8452d659",
|
|
"metadata": {},
|
|
"source": [
|
|
"The following cells contain an implementation of the parallel algorihtm 1 using Julia's Distribtued module. Take a look and try to understand it."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "6d8a1383",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"using Distributed"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "d5842410",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"if procs() == workers()\n",
|
|
" addprocs(4)\n",
|
|
"end"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "2aec1209",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"function matmul_dist_1!(C, A, B)\n",
|
|
" m = size(A,1)\n",
|
|
" n = size(A,2)\n",
|
|
" l = size(B,2)\n",
|
|
" z = zero(eltype(C))\n",
|
|
" @assert nworkers() == m*n\n",
|
|
" iw = 0 \n",
|
|
" @sync for j in 1:l\n",
|
|
" for i in 1:m\n",
|
|
" Ai = A[i,:]\n",
|
|
" Bj = B[:,j]\n",
|
|
" iw += 1\n",
|
|
" w = workers()[iw]\n",
|
|
" ftr = @spawnat w begin\n",
|
|
" Cij = z\n",
|
|
" for k in 1:n\n",
|
|
" 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": "847ade81",
|
|
"metadata": {},
|
|
"source": [
|
|
"You can execute the following cells to test this implementation."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "b920bde0",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"using Test\n",
|
|
"N = 2\n",
|
|
"A = rand(N,N)\n",
|
|
"B = rand(N,N)\n",
|
|
"C = similar(A)\n",
|
|
"@test matmul_dist_1!(C,A,B) ≈ A*B"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "fa8d7f40",
|
|
"metadata": {},
|
|
"source": [
|
|
"### A more practical version of algorithm 1"
|
|
]
|
|
},
|
|
{
|
|
"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(A,1)\n",
|
|
" n = size(A,2)\n",
|
|
" l = size(B,2)\n",
|
|
" z = zero(eltype(C))\n",
|
|
" @sync for j in 1:l\n",
|
|
" for i in 1:m\n",
|
|
" Ai = A[i,:]\n",
|
|
" Bj = B[:,j]\n",
|
|
" # Note the :any\n",
|
|
" ftr = @spawnat :any begin\n",
|
|
" Cij = z\n",
|
|
" for k in 1:n\n",
|
|
" 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 = 40\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": "9f6424c8",
|
|
"metadata": {},
|
|
"source": [
|
|
"Note that each worker will process on average N^2/P entries instead of a single one as in the original implementation. This invalidates our complexity analysis."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "75f30a9c",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div class=\"alert alert-block alert-success\">\n",
|
|
"<b>Question:</b> Which is the computational complexity of the work done in a worker for this variation of algorithm 1?\n",
|
|
"</div>\n",
|
|
"\n",
|
|
" a) O(N/P) communication and O(N^2) computation\n",
|
|
" b) O(N^2) communication and O(N/P) computation\n",
|
|
" c) O(N^3/P) communication and O(N^3/P) computation\n",
|
|
" d) O(N/P) communication and O(N/P) computation"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "76a18834",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"answer = \"x\"\n",
|
|
"alg_1_v2_complex_check(answer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "8bf21c75",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div class=\"alert alert-block alert-success\">\n",
|
|
"<b>Question:</b> For which values of Cn, Cm, Cm the this variation of the parallel algorithm 1 achieves the optimal efficiency?\n",
|
|
"</div>\n",
|
|
"\n",
|
|
" a) Cm == Cn + Cw\n",
|
|
" b) Cm*N^2 == Cn + Cw\n",
|
|
" c) Cm == (Cn + Cw)*N\n",
|
|
" d) Cm == (Cn + Cw)*P\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "6de8d00f",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"answer = \"x\"\n",
|
|
"alg_1_v2_time_check(answer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "3e0d32fc",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Parallel algorithm 2\n",
|
|
"\n",
|
|
"Each worker computes a row of `C`. We need `P=N` workers.\n"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {
|
|
"fig_matmul_intro_q_2.png": {
|
|
"image/png": 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"
|
|
}
|
|
},
|
|
"cell_type": "markdown",
|
|
"id": "9bc5cb16",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div>\n",
|
|
"<img src=\"attachment:fig_matmul_intro_q_2.png\" align=\"left\" width=\"450\"/>\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c9c40141",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Data dependencies"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "b5c89c45",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Complexity"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "caafe52f",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Efficiency"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "78c8c975",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Implementation"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "710edd44",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"function matmul_dist_2!(C, A, B)\n",
|
|
" m = size(A,1)\n",
|
|
" n = size(A,2)\n",
|
|
" l = size(B,2)\n",
|
|
" z = zero(eltype(C))\n",
|
|
" @assert nworkers() == m\n",
|
|
" iw = 0\n",
|
|
" @sync for i in 1:m\n",
|
|
" Ai = A[i,:]\n",
|
|
" iw += 1\n",
|
|
" w = workers()[iw]\n",
|
|
" ftr = @spawnat w begin\n",
|
|
" Ci = fill(z,l)\n",
|
|
" for j in 1:n\n",
|
|
" for k in 1:n\n",
|
|
" Ci[j] += Ai[k]*B[k,j]\n",
|
|
" end\n",
|
|
" end\n",
|
|
" Ci\n",
|
|
" end\n",
|
|
" @async C[i,:] = fetch(ftr)\n",
|
|
" end\n",
|
|
" C\n",
|
|
" end"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "c2378bba",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"using Test\n",
|
|
"N = 4\n",
|
|
"A = rand(N,N)\n",
|
|
"B = rand(N,N)\n",
|
|
"C = similar(A)\n",
|
|
"@test matmul_dist_2!(C,A,B) ≈ A*B"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "30e6cb67",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Parallel algorithm 3"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d294e05a",
|
|
"metadata": {},
|
|
"source": [
|
|
"Each worker computes N/P consecutive rows of `C`."
|
|
]
|
|
},
|
|
{
|
|
"attachments": {
|
|
"fig_matmul_intro_q_3.png": {
|
|
"image/png": 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"
|
|
}
|
|
},
|
|
"cell_type": "markdown",
|
|
"id": "47868612",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div>\n",
|
|
"<img src=\"attachment:fig_matmul_intro_q_3.png\" align=\"left\" width=\"450\"/>\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "0a04b344",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Data dependencies"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "019bd08a",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Complexity"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "93c30b29",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Efficiency"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "4587a17e",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Implementation\n",
|
|
"\n",
|
|
"The implementation of this algorithm is let as an exercise (see below). The following function is a helper to compute which subset of rows will be processed by a given worker."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "3bbf0055",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"function local_rows(i,N)\n",
|
|
" p = i\n",
|
|
" P = nworkers()\n",
|
|
" l = N ÷ P\n",
|
|
" offset = l * (p-1)\n",
|
|
" rem = N % P\n",
|
|
" if rem >= (P-p+1)\n",
|
|
" l = l + 1\n",
|
|
" offset = offset + p - (P-rem) - 1\n",
|
|
" end\n",
|
|
" start = 1+offset\n",
|
|
" stop = l+offset\n",
|
|
" start:stop\n",
|
|
"end"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "768c4c8d",
|
|
"metadata": {},
|
|
"source": [
|
|
"Run the following cell to understand what function `local_rows` does."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "79627008",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"N = 12\n",
|
|
"for i in 1:nworkers()\n",
|
|
" rows = local_rows(i,N)\n",
|
|
" @show rows\n",
|
|
"end"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d67bfd59",
|
|
"metadata": {},
|
|
"source": [
|
|
"Implement the parallel algorithm 3 in the function below. Test it with the provided test."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "efa65ae4",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"function matmul_dist_3!(C, A, B)\n",
|
|
" ## Implement your code here\n",
|
|
" C\n",
|
|
"end"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "b07001dc",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"using Test\n",
|
|
"N = 4\n",
|
|
"A = rand(N,N)\n",
|
|
"B = rand(N,N)\n",
|
|
"C = similar(A)\n",
|
|
"@test matmul_dist_3!(C,A,B) ≈ A*B"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "5e6a89dd",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Efficiency\n",
|
|
"\n",
|
|
"The usual bottleneck in distributed computations is the overhead associated with the communication between processes.\n",
|
|
"\n",
|
|
"As we can see, the distributed function is much slower than the handwritten serial version. The additional runtime is due to the communication between the master process and the worker processes. \n",
|
|
"But how can we analyze the efficiency of this algorithm in general? In order to determine the efficiency, we have to compare how much computation time we can save to how much communication overhead there is in addition to the serial program. \n",
|
|
"\n",
|
|
"### Exercise 4\n",
|
|
"Determine the complexity of the serial algorithm and compare it with the complexity of the coordinator's work in the parallel algorithm. How much compute time does the coordinator save by offloading work to nodes? "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "c800dd33",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"a = \"O(√N)\"\n",
|
|
"b = \"O(N)\"\n",
|
|
"c = \"O(N²)\"\n",
|
|
"d = \"O(N³)\"\n",
|
|
"\n",
|
|
"compute_time_serial = #TODO\n",
|
|
"compute_time_coordinator = #TODO\n",
|
|
"compute_time_saved = #TODO\n",
|
|
"\n",
|
|
"@test (compute_time_serial, compute_time_coordinator, compute_time_saved) == solution_mm_par_04()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "a98a10b9",
|
|
"metadata": {},
|
|
"source": [
|
|
"The complexity of the sequential algorithm is $O(N^3)$. The coordinator loops over the number of workers (= the number of elements in $C$, i.e. $N^2$). The computation of the dot-product is outsourced to the workers. So the complexity of the coordinator's work is $O(N^2)$. Thus, the coordinator saves $\\frac{O(N^2)}{O(N^3)} = O(N)$ compute time."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "1ca84016",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Exercise 5\n",
|
|
"How much communication overhead is there between the coordinator and the workers? In order to determine this, evaluate the size of the data that is passed between coordinator and workers."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "50543fef",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"a = \"O(log(N))\"\n",
|
|
"b = \"O(√N)\"\n",
|
|
"c = \"O(N)\"\n",
|
|
"d = \"O(N²)\"\n",
|
|
"\n",
|
|
"comm_overhead = #TODO\n",
|
|
"@test comm_overhead == solution_mm_par_05()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "432fbe4f",
|
|
"metadata": {},
|
|
"source": [
|
|
"The coordinator sends 2 arrays of length `n` to each worker. After the worker is done computing, it sends 1 float back to the coordinator. So the complexity of the communication overhead is $O(2N +1) = O(N)$.\n",
|
|
"```julia\n",
|
|
"@async C[i,j] = @fetchfrom w dot(Ai, Bj) # <------- Send 2 Arrays of length n to workers, Receive 1 number\n",
|
|
"\n",
|
|
"```"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "9ee43181",
|
|
"metadata": {},
|
|
"source": [
|
|
"In conclusion, we found out that the computation/communication ratio is $O(N)/O(N) = O(1)$, so algorithm 1 provides no gain in efficiency. \n",
|
|
"\n",
|
|
"\n",
|
|
"# Parallel Algorithm 2\n",
|
|
"Another idea to parallelize the matrix multiplication is to let each processor compute one _row_ ($N$ elements) of $C$. This approach requires only $N$ workers. \n",
|
|
"\n",
|
|
"## Data Dependencies"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "dc2d1264",
|
|
"metadata": {},
|
|
"source": [
|
|
"Each worker requires the entire $B$ matrix and one row of $A$ as an input. Each processor can re-use the row of A $N$-times to compute the $N$-elements in its row. \n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "4415d376",
|
|
"metadata": {},
|
|
"source": [
|
|
"The image below depicts the structure of Parallel Algorithm 2. \n",
|
|
"The coordinator sends the $i$-th row of $A$ and the entire matrix B to worker node $i$. The worker nodes compute the whole row and send the result back to the coordinator.\n",
|
|
"\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "3ace3b41",
|
|
"metadata": {},
|
|
"source": [
|
|
"Let's first remove all unnecessary workers. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "8547d6c1",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rmprocs(6:26)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "13a3ab88",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"workers()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "8b3ccbe4",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Exercise 6\n",
|
|
"Provide the code for Parallel Algorithm 2. Test your algorithm using Julia's `@test` macro. You can view the solution to this exercise at the bottom of the notebook. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "68d34ae0",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"function matmul_dist2!(C, A, B)\n",
|
|
" n = size(A,1)\n",
|
|
" @assert size(A,2) == n\n",
|
|
" @assert size(B,1) == n\n",
|
|
" @assert size(B,2) == n\n",
|
|
" @assert size(C,1) == n\n",
|
|
" @assert size(C,2) == n\n",
|
|
" @assert nworkers() == n\n",
|
|
" \n",
|
|
" # TODO: finish the code for algorithm 2\n",
|
|
" \n",
|
|
" C\n",
|
|
"end \n",
|
|
"\n",
|
|
"# TODO: test your code"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "2e9e7902",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Efficiency\n",
|
|
"\n",
|
|
"### Exercise 7\n",
|
|
"How efficient is Parallel Algorithm 2? Answer the following questions to determine the computation/communication ratio: \n",
|
|
"1. How much compute time does the coordinator save by offloading work to nodes?\n",
|
|
"2. How much communication overhead is there between the coordinator and the workers?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "e0ee39a7",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"a = \"O(1)\"\n",
|
|
"b = \"O(√N)\"\n",
|
|
"c = \"O(N)\"\n",
|
|
"d = \"O(N²)\"\n",
|
|
"e = \"O(N³)\"\n",
|
|
"\n",
|
|
"compute_time_saved = #TODO\n",
|
|
"comm_overhead = #TODO\n",
|
|
"comp_comm_ratio = #TODO\n",
|
|
"\n",
|
|
"@test (compute_time_saved, comm_overhead, comp_comm_ratio) == solution_mm_par_07()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "57092027",
|
|
"metadata": {},
|
|
"source": [
|
|
" 1. The coordinator saves $O(N^2)$ computations. \n",
|
|
" 2. The coordinator sends $N + N^2$ floats to each worker and receives $N$ floats from each worker. Thus, the communication overhead is $O(2N + N^2) = O(N^2)$. \n",
|
|
" \n",
|
|
"This results in a computation/communication ratio of $O(N^2)/O(N^2) = O(1)$. \n",
|
|
"\n",
|
|
"```julia\n",
|
|
"\n",
|
|
"@async C[i,:] = @fetchfrom w Ai*B # <----- Worker does N² computations/ Send N + N² floats, receive N floats \n",
|
|
"```"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d02d9321",
|
|
"metadata": {},
|
|
"source": [
|
|
"To conclude, our second algorithm is still inefficient. How can we design an algorithm that does more computation than communication? \n",
|
|
"\n",
|
|
"# Parallel Algorithm 3\n",
|
|
"\n",
|
|
"Think of how to design an efficient algorithm to solve matrix multiplication in parallel. Hint: Assume that we are dealing with large problem sizes, thus $N >> P$ (where $P$ is the number of processors). "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "def4265c",
|
|
"metadata": {},
|
|
"source": [
|
|
"If $N >> P$, we can assign _many rows_ to one processor. Each processor computes $N/P$ rows of $C$. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "a6d4bab7",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Data Dependencies"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "7fd516bb",
|
|
"metadata": {},
|
|
"source": [
|
|
"Each processor needs the entire $B$ matrix and $N/P$ rows of $A$. \n",
|
|
"\n",
|
|
""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "3b67c60a",
|
|
"metadata": {},
|
|
"source": [
|
|
"Now let's start coding Algorithm 3. We'll use $P = 2$, so let's remove all other workers. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "6f6d154e",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rmprocs(4:5)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "7511e301",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"workers()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "bfc5dddc",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Exercise 8\n",
|
|
"Write a function that calculates the row indices for each partition of matrix $A$. The function should store the result in the input variable `indices`, which is of the type `Array{Int64}[]` (a list of arrays). You can add arrays to this variable by calling `push!(indices, my_array)`. The $i$-th entry of `indices` should be an array of row indices of matrix $A$ for the $i$-th worker. The function also receives the number of rows `n` and the number of processors `p` as input parameters. You can assume that `p` divides `n` without remainder, thus $n \\pmod p = 0$."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "4375aa9f",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"function calculate_partition!(indices, n, p)\n",
|
|
" @assert mod(n,p) == 0\n",
|
|
" # TODO: Calculate the row indices of matrix A for each worker. \n",
|
|
" # Store arrays of row indices in the variable indices.\n",
|
|
" \n",
|
|
"end"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "57b766cb",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"indices = Array{Int64}[]\n",
|
|
"n = 8\n",
|
|
"p = 2\n",
|
|
"calculate_partition!(indices, n, p)\n",
|
|
"@test indices == [[1,2,3,4], [5,6,7,8]]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "7a0ebd30",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Exercise 9\n",
|
|
"Provide the code for Parallel Algorithm 3. Test your implementation using Julia macro `@test`. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "7d7c0193",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"function matmul_dist3!(C, A, B)\n",
|
|
" n = size(A,1)\n",
|
|
" p = nworkers()\n",
|
|
" @assert size(A,2) == n\n",
|
|
" @assert size(B,1) == n\n",
|
|
" @assert size(B,2) == n\n",
|
|
" @assert size(C,1) == n\n",
|
|
" @assert size(C,2) == n\n",
|
|
" @assert mod(n, p) == 0\n",
|
|
" indices = Array{Int64}[]\n",
|
|
" calculate_partition!(indices, n, p)\n",
|
|
" @sync for (i, w) in enumerate(workers())\n",
|
|
" # TODO: do matrix multiplication in parallel\n",
|
|
" end\n",
|
|
" C\n",
|
|
"end\n",
|
|
"\n",
|
|
"# TODO: test your solution"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "cfc6bc65",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Efficiency again\n",
|
|
"Let's have a look at the efficiency of Parallel Algorithm 3. \n",
|
|
"\n",
|
|
"### Exercise 10\n",
|
|
"How efficient is this parallel algorithm? Determine the computation/communication ratio like in Exercise 7. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "8a1efe1f",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"a = \"O(1)\"\n",
|
|
"b = \"O(N/P)\"\n",
|
|
"c = \"O(N)\"\n",
|
|
"d = \"O(N²/P)\"\n",
|
|
"e = \"O(N²)\"\n",
|
|
"f = \"O(N³/P)\"\n",
|
|
"g = \"O(N³)\"\n",
|
|
"\n",
|
|
"compute_time_saved = #TODO\n",
|
|
"comm_overhead = #TODO\n",
|
|
"comp_comm_ratio = #TODO\n",
|
|
"\n",
|
|
"@test (compute_time_saved, comm_overhead, comp_comm_ratio) == solution_mm_par_10()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "f548576d",
|
|
"metadata": {},
|
|
"source": [
|
|
"- The workers now do $N/P \\times N \\times N$ computations, thus the complexity is $O(N³/P)$. \n",
|
|
"\n",
|
|
"- The coordinator sends the whole $B$ matrix ($N²$) and a part of the $A$ matrix ($N²/P$) and receives part of the $C$ matrix ($N²/P$). Hence, the complexity of the communication overhead is $O(N²/P)$. \n",
|
|
"- Finally, we obtain a computation/communication ratio of $O(N³/P) / O(N²/P) = O(N/P)$. \n",
|
|
"\n",
|
|
"\n",
|
|
"```julia\n",
|
|
"\n",
|
|
"@async C[rows_w,:] = @fetchfrom w Aw*B # <----- Send N² + N²/P floats, get N²/P floats. Worker does N/P * N * N multiplications/additions. \n",
|
|
"```"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "5ca61b7f",
|
|
"metadata": {},
|
|
"source": [
|
|
"The table below compares the three parallel algorithms. \n",
|
|
"\n",
|
|
"<div id = \"div1\"></div>\n",
|
|
"\n",
|
|
"| Algorithm | Parallelism <br>(#jobs) | Communication <br>per job | Computation <br>per job | Ratio computation/<br>communication |\n",
|
|
"|---|---|---|---|---|\n",
|
|
"| 1 | N² | 2N + 1 | N | O(1) |\n",
|
|
"| 2 | N | N + N² | N² | O(1) |\n",
|
|
"| 3 | P | N²/P + N² + N²/P | N³/P | O(N/P) |"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "2eaaa1da",
|
|
"metadata": {
|
|
"tags": [
|
|
"hide_cell"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"HTML(\"\"\"\n",
|
|
"<style>\n",
|
|
"#div1 + table{\n",
|
|
" font-size: 16px;\n",
|
|
" border: 1px solid;\n",
|
|
" width: 80%;\n",
|
|
"}\n",
|
|
"\n",
|
|
"#div1 + table thead tr{\n",
|
|
" background-color: #A5F1C2\n",
|
|
"}\n",
|
|
"\n",
|
|
"#div1 + table tbody tr:nth-child(even){\n",
|
|
" background-color: #CBF0D9\n",
|
|
"}\n",
|
|
"\n",
|
|
"#div1 + table tbody tr:nth-child(odd){\n",
|
|
" background-color: #E7EBE8\n",
|
|
"}\n",
|
|
"</style>\"\"\")\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e5f0c0cb",
|
|
"metadata": {},
|
|
"source": [
|
|
"To conclude, only algorithm 3 provides an increase in efficiency, especially for large problem sizes. If $N >> P$, algorithm 3 will have a low communication overhead. \n",
|
|
"\n",
|
|
"## Scalability \n",
|
|
"The matrices we have looked at in the previous examples were of a very small size. In the previous paragraph, we have established that the efficiency for Algorithm 3 is better especially for large matrices. Let's examine this by running the algorithm for different matrix sizes. First we will construct three matrices of different sizes. Next, we run the parallel algorithm for different numbers of processors. We evaluate the performance against the handwritten sequential version `matmul_hand!()` presented at the top of the notebook. We measure the performance by calculating the _speedup_, which is defined as \n",
|
|
"\n",
|
|
"$$\n",
|
|
"S_p = \\frac{T_1}{T_p},\n",
|
|
"$$\n",
|
|
"\n",
|
|
"where $T_1$ denotes the runtimes of the sequential algorithm on one node, $T_p$ denotes the runtime of the parallel algorithm on $p$ nodes. The ideal speedup is $S_p = p$. We will look more into speedups and how to measure efficiency in a later section of this course. NB: the following cells take about 6 - 10 minutes of compute time. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "7c2b1423",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"matrix_sizes = [84, 420, 2100]\n",
|
|
"\n",
|
|
"function initialize_matrices!(array_A, array_B, array_C)\n",
|
|
" for n in matrix_sizes\n",
|
|
" A = rand(n, n)\n",
|
|
" B = rand(n, n)\n",
|
|
" C = rand(n, n)\n",
|
|
" push!(array_A, A)\n",
|
|
" push!(array_B, B)\n",
|
|
" push!(array_C, C)\n",
|
|
" end\n",
|
|
"end\n",
|
|
"\n",
|
|
"matrices_A = Matrix{Float64}[]\n",
|
|
"matrices_B = Matrix{Float64}[]\n",
|
|
"matrices_C = Matrix{Float64}[]\n",
|
|
"\n",
|
|
"initialize_matrices!(matrices_A, matrices_B, matrices_C)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "2b3feb47",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"n_workers = [0, 1, 2, 4, 7]\n",
|
|
"rmprocs(workers())\n",
|
|
"runtimes = zeros(length(n_workers), length(matrix_sizes));\n",
|
|
"\n",
|
|
" \n",
|
|
"for (i,p) in enumerate(n_workers)\n",
|
|
" # Add sufficient worker processes\n",
|
|
" if p >= 1\n",
|
|
" addprocs(p)\n",
|
|
" end\n",
|
|
" for (j,n) in enumerate(matrix_sizes)\n",
|
|
" @show p, n\n",
|
|
" C = matrices_C[j]\n",
|
|
" A = matrices_A[j]\n",
|
|
" B = matrices_B[j]\n",
|
|
" if nprocs() == 1\n",
|
|
" # Run sequential algorithm if 0 workers\n",
|
|
" runtimes[i,j] = @belapsed matmul_hand!(C, A, B)\n",
|
|
" else\n",
|
|
" runtimes[i,j] = @belapsed matmul_dist3!(C, A, B)\n",
|
|
" end\n",
|
|
" @show runtimes[i,j]\n",
|
|
" end\n",
|
|
"end"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "153ca8a0",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Plot runtimes \n",
|
|
"using Plots\n",
|
|
"\n",
|
|
"function calculate_speedups!(speedup, runtimes, reference)\n",
|
|
" @assert length(reference) == size(runtimes,2)\n",
|
|
" @assert size(speedup,1) == size(runtimes,1)\n",
|
|
" @assert size(speedup,2) == size(runtimes,2)\n",
|
|
" for j in 1:size(runtimes,2)\n",
|
|
" for i in 1:size(runtimes,1)\n",
|
|
" Sₚ = reference[j] / runtimes[i,j] \n",
|
|
" speedup[i,j] = Sₚ\n",
|
|
" end\n",
|
|
" end\n",
|
|
"end\n",
|
|
"\n",
|
|
"speedups = zeros(length(n_workers)-1, length(matrix_sizes))\n",
|
|
"reference = runtimes[1,:]\n",
|
|
"calculate_speedups!(speedups, runtimes[2:end, :], reference)\n",
|
|
"\n",
|
|
"plot(n_workers[2:end], \n",
|
|
" [speedups[:,1], speedups[:,2], speedups[:,3]], \n",
|
|
" label=[\"N=$(matrix_sizes[1])\" \"N=$(matrix_sizes[2])\" \"N=$(matrix_sizes[3])\"],\n",
|
|
" xlabel=\"# workers\", ylabel=\"speedup\",\n",
|
|
" title=\"Speedups compared with sequential version matmul_hand!\")\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "39cd37f0",
|
|
"metadata": {},
|
|
"source": [
|
|
"As we can see, the speedups are higher for larger matrix sizes. We can also observe that the speedups decrease when using more processors. The speedups are superlinear, that is, much larger than the theoretical ideal speedup. The reason for this is that we compare to a very slow handwritten solution whilst using the optimized Julia built-in function ` * ` on the workers in the parallel computation. If we were to compare the performance with Julia's `A * B`, we would see that the parallel algorithm performs worse even for large matrix sizes, since this function is highly optimized. We will get more into the efficiency analysis of parallel algorithms later in the course.\n",
|
|
"\n",
|
|
"# Discussion\n",
|
|
"The first problem we have looked at, Matrix Multiplication, can be parallelized by sending parts of the input matrices to each worker and collecting the result at the end. This problem is **trivial to parallelize**, since the workers can do their computations independently from one another. In the following sections, we will look at other algorithms which require intermediate communication. \n",
|
|
"\n",
|
|
"Another key insight is that we can attain a better performance by chosing a **large grain size**, thus by dividing the matrix $A$ into larger chunks of data.\n",
|
|
"\n",
|
|
"Finally, the time saved by doing more computation than communication is increased in **large problem sizes**. \n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "0de5e8ac",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Solution to Exercises\n",
|
|
"### Solution to Exercise 3\n",
|
|
"\n",
|
|
"The following cell shows the code for the first parallel algorithm with the correct macros."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "4e60911e",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"@everywhere using LinearAlgebra \n",
|
|
"\n",
|
|
"function matmul_dist1!(C, A, B)\n",
|
|
" n = size(A,1)\n",
|
|
" @assert size(A,2) == n\n",
|
|
" @assert size(B,1) == n\n",
|
|
" @assert size(B,2) == n\n",
|
|
" @assert size(C,1) == n\n",
|
|
" @assert size(C,2) == n\n",
|
|
" @assert nworkers() == n^2 \n",
|
|
" # Let each worker compute one element \n",
|
|
" @sync for w in workers()\n",
|
|
" # Compute row and column index from worker id\n",
|
|
" i, j = index_from_wid(w)\n",
|
|
" Ai = A[i,:]\n",
|
|
" Bj = B[:,j]\n",
|
|
" # Do element computation in parallel\n",
|
|
" @async C[i,j] = @fetchfrom w dot(Ai, Bj)\n",
|
|
" end\n",
|
|
" C\n",
|
|
"end"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "4e5f3056",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Solution to Exercise 6\n",
|
|
"The cell below contains the code for parallel algorithm 2."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "72c86b2e",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"function matmul_dist2!(C, A, B)\n",
|
|
" n = size(A,1)\n",
|
|
" @assert size(A,2) == n\n",
|
|
" @assert size(B,1) == n\n",
|
|
" @assert size(B,2) == n\n",
|
|
" @assert size(C,1) == n\n",
|
|
" @assert size(C,2) == n\n",
|
|
" @assert nworkers() == n\n",
|
|
" # Let each worker compute one row \n",
|
|
" @sync for w in workers()\n",
|
|
" # Compute row index from worker id \n",
|
|
" i = w - 1\n",
|
|
" # Make sure Ai is an array, not a vector\n",
|
|
" Ai = A[[i],:]\n",
|
|
" # Do row computation in parallel\n",
|
|
" @async C[i,:] = @fetchfrom w Ai*B\n",
|
|
" end\n",
|
|
" C\n",
|
|
"end\n",
|
|
"\n",
|
|
"# Test solution\n",
|
|
"C1 = matmul_dist2!(C, A, B)\n",
|
|
"@test C1 ≈ A * B"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "21fa8bbc",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Solution to Exercise 8"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "7ed7e697",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"function calculate_partition!(indices, n, p)\n",
|
|
" @assert mod(n,p) == 0\n",
|
|
" nrows = div(n,p)\n",
|
|
" for i in 1:p\n",
|
|
" range =((i-1) * nrows + 1) : (i*nrows)\n",
|
|
" push!(indices, range)\n",
|
|
" end\n",
|
|
"end\n",
|
|
" "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "666bd69c",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Solution to Exercise 9\n",
|
|
"The following cell contains the code for parallel algorithm 3."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "b40d341c",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"function matmul_dist3!(C, A, B)\n",
|
|
" n = size(A,1)\n",
|
|
" p = nworkers()\n",
|
|
" @assert size(A,2) == n\n",
|
|
" @assert size(B,1) == n\n",
|
|
" @assert size(B,2) == n\n",
|
|
" @assert size(C,1) == n\n",
|
|
" @assert size(C,2) == n\n",
|
|
" @assert mod(n, p) == 0\n",
|
|
" indices = Array{Int64}[]\n",
|
|
" calculate_partition!(indices, n, p)\n",
|
|
" @sync for (i, w) in enumerate(workers())\n",
|
|
" # Get row indices of this partition\n",
|
|
" rows_w = indices[i]\n",
|
|
" Aw = A[rows_w,:]\n",
|
|
" # Do partition computation in parallel\n",
|
|
" @async C[rows_w,:] = @fetchfrom w Aw*B\n",
|
|
" end\n",
|
|
" C\n",
|
|
"end\n",
|
|
"\n",
|
|
"# Test solution\n",
|
|
"C1 = matmul_dist3!(C, A, B)\n",
|
|
"@test C1 ≈ A * B"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "066b5483",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Solutions"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "161421d7",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"function matmul_dist_1!(C, A, B)\n",
|
|
" m = size(A,1)\n",
|
|
" n = size(A,2)\n",
|
|
" l = size(B,2)\n",
|
|
" @assert size(B,1) == n\n",
|
|
" @assert size(C,1) == m\n",
|
|
" @assert size(C,2) == l\n",
|
|
" @sync for j in 1:l # Note the @sync!\n",
|
|
" for i in 1:m\n",
|
|
" Ai = A[i,:]\n",
|
|
" Bj = B[:,j]\n",
|
|
" # Compute this entry in any of the available workers \n",
|
|
" @async C[i,j] = fetch(@spawnat :any dot(Ai, Bj))\n",
|
|
" end\n",
|
|
" end\n",
|
|
" C\n",
|
|
"end"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "e1248015",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Julia 1.9.0",
|
|
"language": "julia",
|
|
"name": "julia-1.9"
|
|
},
|
|
"language_info": {
|
|
"file_extension": ".jl",
|
|
"mimetype": "application/julia",
|
|
"name": "julia",
|
|
"version": "1.9.0"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|