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genkuroki/Octavian M=K=N=1024.ipynb

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Octavian!
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Octavian M=K=N=1024.ipynb
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Octavian M=K=N=128.ipynb
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Octavian M=K=N=32.ipynb
{
"cells": [
{
"metadata": {},
"cell_type": "markdown",
"source": "https://discourse.julialang.org/t/intel-c-c-compiler-performance-versus-julia/61929/18"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "versioninfo()",
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": "Julia Version 1.7.0-DEV.1129\nCommit 9117b4d6d6 (2021-05-20 16:42 UTC)\nPlatform Info:\n OS: Windows (x86_64-w64-mingw32)\n CPU: Intel(R) Core(TM) i7-10750H CPU @ 2.60GHz\n WORD_SIZE: 64\n LIBM: libopenlibm\n LLVM: libLLVM-11.0.1 (ORCJIT, skylake)\nEnvironment:\n JULIA_NUM_THREADS = 12\n JULIA_PYTHONCALL_EXE = C:\\Users\\genkuroki\\.julia\\conda\\3\\python.exe\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "using LinearAlgebra\nusing BLASBenchmarksCPU\nusing Octavian\nusing BenchmarkHistograms",
"execution_count": 2,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "M = K = N = 32\nA = rand(M, K)\nB = rand(K, N)\nC1 = @time(A * B)\nC0 = similar(C1);",
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": " 0.539203 seconds (2.53 M allocations: 134.135 MiB, 7.92% gc time, 99.95% compilation time)\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "@benchmark mul!($C0, $A, $B)",
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 4,
"data": {
"text/plain": "samples: 10000; evals/sample: 9; memory estimate: 0 bytes; allocs estimate: 0\nns\n\n (2290.0 - 2700.0 ] \u001b[32m██████████████████████████████ \u001b[39m9341\n (2700.0 - 3100.0 ] \u001b[32m█▏\u001b[39m350\n (3100.0 - 3510.0 ] \u001b[32m▏\u001b[39m8\n (3510.0 - 3920.0 ] \u001b[32m▏\u001b[39m19\n (3920.0 - 4330.0 ] \u001b[32m▍\u001b[39m79\n (4330.0 - 4740.0 ] \u001b[32m▎\u001b[39m51\n (4740.0 - 5140.0 ] \u001b[32m▏\u001b[39m38\n (5140.0 - 5550.0 ] \u001b[32m▏\u001b[39m16\n (5550.0 - 5960.0 ] \u001b[32m▏\u001b[39m31\n (5960.0 - 6370.0 ] \u001b[32m▏\u001b[39m20\n (6370.0 - 6780.0 ] \u001b[32m▏\u001b[39m14\n (6780.0 - 7180.0 ] \u001b[32m▏\u001b[39m15\n (7180.0 - 7590.0 ] \u001b[32m▏\u001b[39m5\n (7590.0 - 8000.0 ] \u001b[32m▏\u001b[39m3\n (8000.0 - 10600.0] \u001b[32m▏\u001b[39m10\n\n Counts\n\nmin: 2.289 μs (0.00% GC); mean: 2.426 μs (0.00% GC); median: 2.311 μs (0.00% GC); max: 10.600 μs (0.00% GC)."
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# MKL dgemm\n@benchmark gemmmkl!($C0, $A, $B)",
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 5,
"data": {
"text/plain": "samples: 10000; evals/sample: 10; memory estimate: 0 bytes; allocs estimate: 0\nns\n\n (1290.0 - 1600.0] \u001b[32m██████████████████████████████ \u001b[39m9490\n (1600.0 - 1910.0] \u001b[32m█▎\u001b[39m356\n (1910.0 - 2220.0] \u001b[32m▏\u001b[39m4\n (2220.0 - 2530.0] \u001b[32m▏\u001b[39m2\n (2530.0 - 2840.0] \u001b[32m▏\u001b[39m17\n (2840.0 - 3150.0] \u001b[32m▎\u001b[39m40\n (3150.0 - 3470.0] \u001b[32m▏\u001b[39m11\n (3470.0 - 3780.0] \u001b[32m▏\u001b[39m16\n (3780.0 - 4090.0] \u001b[32m▏\u001b[39m6\n (4090.0 - 4400.0] \u001b[32m▏\u001b[39m10\n (4400.0 - 4710.0] \u001b[32m▏\u001b[39m14\n (4710.0 - 5020.0] \u001b[32m▏\u001b[39m8\n (5020.0 - 5330.0] \u001b[32m▏\u001b[39m9\n (5330.0 - 5640.0] \u001b[32m▏\u001b[39m7\n (5640.0 - 6950.0] \u001b[32m▏\u001b[39m10\n\n Counts\n\nmin: 1.290 μs (0.00% GC); mean: 1.370 μs (0.00% GC); median: 1.300 μs (0.00% GC); max: 6.950 μs (0.00% GC)."
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# MKL dgemm_direct\n@benchmark gemmmkl_direct!($C0, $A, $B)",
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 6,
"data": {
"text/plain": "samples: 10000; evals/sample: 10; memory estimate: 0 bytes; allocs estimate: 0\nns\n\n (1200.0 - 1520.0] \u001b[32m██████████████████████████████\u001b[39m9321\n (1520.0 - 1830.0] \u001b[32m█▌\u001b[39m430\n (1830.0 - 2150.0] \u001b[32m▏\u001b[39m19\n (2150.0 - 2470.0] \u001b[32m▏\u001b[39m25\n (2470.0 - 2780.0] \u001b[32m▏\u001b[39m34\n (2780.0 - 3100.0] \u001b[32m▎\u001b[39m47\n (3100.0 - 3420.0] \u001b[32m▏\u001b[39m29\n (3420.0 - 3730.0] \u001b[32m▏\u001b[39m31\n (3730.0 - 4050.0] \u001b[32m▏\u001b[39m14\n (4050.0 - 4360.0] \u001b[32m▏\u001b[39m7\n (4360.0 - 4680.0] \u001b[32m▏\u001b[39m7\n (4680.0 - 5000.0] \u001b[32m▏\u001b[39m8\n (5000.0 - 5310.0] \u001b[32m▏\u001b[39m10\n (5310.0 - 5630.0] \u001b[32m▏\u001b[39m8\n (5630.0 - 7420.0] \u001b[32m▏\u001b[39m10\n\n Counts\n\nmin: 1.200 μs (0.00% GC); mean: 1.302 μs (0.00% GC); median: 1.220 μs (0.00% GC); max: 7.420 μs (0.00% GC)."
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# Octavian.jl\n@benchmark matmul!($C0, $A, $B)",
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 7,
"data": {
"text/plain": "samples: 10000; evals/sample: 142; memory estimate: 0 bytes; allocs estimate: 0\nns\n\n (700.0 - 790.0 ] \u001b[32m██████████████████████████████ \u001b[39m8213\n (790.0 - 870.0 ] \u001b[32m██▌\u001b[39m661\n (870.0 - 960.0 ] \u001b[32m█\u001b[39m249\n (960.0 - 1040.0] \u001b[32m█▏\u001b[39m283\n (1040.0 - 1130.0] \u001b[32m▋\u001b[39m152\n (1130.0 - 1210.0] \u001b[32m▋\u001b[39m137\n (1210.0 - 1300.0] \u001b[32m▍\u001b[39m82\n (1300.0 - 1380.0] \u001b[32m▍\u001b[39m78\n (1380.0 - 1470.0] \u001b[32m▎\u001b[39m48\n (1470.0 - 1550.0] \u001b[32m▏\u001b[39m31\n (1550.0 - 1640.0] \u001b[32m▏\u001b[39m10\n (1640.0 - 1720.0] \u001b[32m▏\u001b[39m13\n (1720.0 - 1800.0] \u001b[32m▏\u001b[39m20\n (1800.0 - 1890.0] \u001b[32m▏\u001b[39m13\n (1890.0 - 2380.0] \u001b[32m▏\u001b[39m10\n\n Counts\n\nmin: 704.225 ns (0.00% GC); mean: 781.582 ns (0.00% GC); median: 727.465 ns (0.00% GC); max: 2.385 μs (0.00% GC)."
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "using StaticArrays\nAm = MMatrix{M, K}(A)\nBm = MMatrix{K, N}(B)\nCm = similar(Am);",
"execution_count": 8,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "@benchmark mul!($Cm, $Am, $Bm)",
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 9,
"data": {
"text/plain": "samples: 10000; evals/sample: 9; memory estimate: 0 bytes; allocs estimate: 0\nns\n\n (2270.0 - 2700.0 ] \u001b[32m██████████████████████████████▏\u001b[39m7222\n (2700.0 - 3130.0 ] \u001b[32m███▌\u001b[39m826\n (3130.0 - 3570.0 ] \u001b[32m█▍\u001b[39m329\n (3570.0 - 4000.0 ] \u001b[32m███▉\u001b[39m913\n (4000.0 - 4440.0 ] \u001b[32m█▋\u001b[39m362\n (4440.0 - 4870.0 ] \u001b[32m▋\u001b[39m123\n (4870.0 - 5310.0 ] \u001b[32m▎\u001b[39m52\n (5310.0 - 5740.0 ] \u001b[32m▎\u001b[39m32\n (5740.0 - 6170.0 ] \u001b[32m▏\u001b[39m27\n (6170.0 - 6610.0 ] \u001b[32m▎\u001b[39m31\n (6610.0 - 7040.0 ] \u001b[32m▏\u001b[39m23\n (7040.0 - 7480.0 ] \u001b[32m▏\u001b[39m11\n (7480.0 - 7910.0 ] \u001b[32m▏\u001b[39m25\n (7910.0 - 8340.0 ] \u001b[32m▏\u001b[39m14\n (8340.0 - 33570.0] \u001b[32m▏\u001b[39m10\n\n Counts\n\nmin: 2.267 μs (0.00% GC); mean: 2.850 μs (0.00% GC); median: 2.511 μs (0.00% GC); max: 33.567 μs (0.00% GC)."
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# StaticArrays.MMatrix are statically sized\n@benchmark matmul!($Cm, $Am, $Bm)",
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 10,
"data": {
"text/plain": "samples: 10000; evals/sample: 157; memory estimate: 0 bytes; allocs estimate: 0\nns\n\n (670.0 - 760.0 ] \u001b[32m██████████████████████████████ \u001b[39m7928\n (760.0 - 850.0 ] \u001b[32m█▍\u001b[39m360\n (850.0 - 940.0 ] \u001b[32m█▏\u001b[39m277\n (940.0 - 1030.0] \u001b[32m█▊\u001b[39m441\n (1030.0 - 1130.0] \u001b[32m█\u001b[39m257\n (1130.0 - 1220.0] \u001b[32m█▋\u001b[39m417\n (1220.0 - 1310.0] \u001b[32m▌\u001b[39m119\n (1310.0 - 1400.0] \u001b[32m▍\u001b[39m71\n (1400.0 - 1490.0] \u001b[32m▎\u001b[39m55\n (1490.0 - 1580.0] \u001b[32m▏\u001b[39m23\n (1580.0 - 1680.0] \u001b[32m▏\u001b[39m17\n (1680.0 - 1770.0] \u001b[32m▏\u001b[39m14\n (1770.0 - 1860.0] \u001b[32m▏\u001b[39m7\n (1860.0 - 1950.0] \u001b[32m▏\u001b[39m4\n (1950.0 - 2730.0] \u001b[32m▏\u001b[39m10\n\n Counts\n\nmin: 668.790 ns (0.00% GC); mean: 785.391 ns (0.00% GC); median: 714.013 ns (0.00% GC); max: 2.734 μs (0.00% GC)."
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
}
],
"metadata": {
"@webio": {
"lastKernelId": null,
"lastCommId": null
},
"kernelspec": {
"name": "julia-1.7-depwarn-o3",
"display_name": "Julia 1.7.0-DEV depwarn -O3",
"language": "julia"
},
"language_info": {
"file_extension": ".jl",
"name": "julia",
"mimetype": "application/julia",
"version": "1.7.0"
},
"toc": {
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"base_numbering": 1,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
},
"gist": {
"id": "6123aef79488bc20b52047656fc6f015",
"data": {
"description": "Octavian",
"public": true
}
},
"_draft": {
"nbviewer_url": "https://gist.github.com/6123aef79488bc20b52047656fc6f015"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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