From 4e338ab748e6366c27a0fc15ff22e81652188a3e Mon Sep 17 00:00:00 2001 From: Oli Date: Thu, 16 Dec 2021 18:11:24 -0300 Subject: [PATCH] plot memory --- fftma_module/gen/analysis.ipynb | 1208 ++++++++++++++++++------------- 1 file changed, 708 insertions(+), 500 deletions(-) diff --git a/fftma_module/gen/analysis.ipynb b/fftma_module/gen/analysis.ipynb index 1daea73..60ed061 100644 --- a/fftma_module/gen/analysis.ipynb +++ b/fftma_module/gen/analysis.ipynb @@ -2,16 +2,18 @@ "cells": [ { "cell_type": "code", - "execution_count": 35, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ - "import pandas as pd " + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np " ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -21,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 28, "metadata": { "scrolled": true }, @@ -47,149 +49,149 @@ " \n", " \n", " \n", + " function_name\n", " elapsed_time\n", " executions\n", - " function_name\n", " used_virtual_mem\n", " \n", " \n", " \n", " \n", " 0\n", - " 0.000456\n", + " Py_getvalues\n", + " 0.000007\n", " 1\n", - " cgrid\n", - " 2877.4\n", + " 0.0\n", " \n", " \n", " 1\n", - " 0.180264\n", - " 1\n", - " Py_kgeneration\n", - " 2877.9\n", + " ran2\n", + " 0.000065\n", + " 41552\n", + " 240.5\n", " \n", " \n", " 2\n", - " 0.001184\n", - " 512\n", " gasdev\n", - " 2882.6\n", + " 0.000383\n", + " 32768\n", + " 240.5\n", " \n", " \n", " 3\n", - " 0.000081\n", - " 700\n", - " cov_value\n", - " 2877.9\n", + " generate\n", + " 2.055015\n", + " 1\n", + " 219.2\n", " \n", " \n", " 4\n", - " 0.000015\n", - " 1\n", - " clean_real\n", - " 2877.9\n", + " maxfactor\n", + " 0.000005\n", + " 5\n", + " 219.2\n", " \n", " \n", " 5\n", - " 0.077514\n", - " 1\n", - " covariance\n", - " 2877.9\n", + " length\n", + " 0.000071\n", + " 3\n", + " 219.2\n", " \n", " \n", " 6\n", - " 0.079166\n", + " cgrid\n", + " 0.000265\n", " 1\n", - " fftma2\n", - " 2877.9\n", + " 219.2\n", " \n", " \n", " 7\n", - " 0.100123\n", - " 1\n", - " generate\n", - " 2877.4\n", + " cov_value\n", + " 0.000156\n", + " 24624\n", + " 234.7\n", " \n", " \n", " 8\n", - " 0.000098\n", - " 3\n", - " length\n", - " 2877.4\n", + " covariance\n", + " 0.839737\n", + " 1\n", + " 228.0\n", " \n", " \n", " 9\n", - " 0.000034\n", - " 1\n", - " Py_getvalues\n", - " 0.0\n", + " fourt\n", + " 0.003451\n", + " 3\n", + " 227.9\n", " \n", " \n", " 10\n", - " 0.000037\n", - " 702\n", - " ran2\n", - " 2882.6\n", + " prebuild_gwn\n", + " 0.000138\n", + " 1\n", + " 227.4\n", " \n", " \n", " 11\n", - " 0.000193\n", - " 3\n", - " fourt\n", - " 2877.9\n", + " build_real\n", + " 0.000424\n", + " 1\n", + " 227.4\n", " \n", " \n", " 12\n", - " 0.000033\n", + " clean_real\n", + " 0.000158\n", " 1\n", - " build_real\n", - " 2877.9\n", + " 227.4\n", " \n", " \n", " 13\n", - " 0.000034\n", + " fftma2\n", + " 0.849271\n", " 1\n", - " prebuild_gwn\n", - " 2877.9\n", + " 227.4\n", " \n", " \n", " 14\n", - " 0.000010\n", - " 3\n", - " maxfactor\n", - " 2877.4\n", + " Py_kgeneration\n", + " 2.904626\n", + " 1\n", + " 227.4\n", " \n", " \n", "\n", "" ], "text/plain": [ - " elapsed_time executions function_name used_virtual_mem\n", - "0 0.000456 1 cgrid 2877.4\n", - "1 0.180264 1 Py_kgeneration 2877.9\n", - "2 0.001184 512 gasdev 2882.6\n", - "3 0.000081 700 cov_value 2877.9\n", - "4 0.000015 1 clean_real 2877.9\n", - "5 0.077514 1 covariance 2877.9\n", - "6 0.079166 1 fftma2 2877.9\n", - "7 0.100123 1 generate 2877.4\n", - "8 0.000098 3 length 2877.4\n", - "9 0.000034 1 Py_getvalues 0.0\n", - "10 0.000037 702 ran2 2882.6\n", - "11 0.000193 3 fourt 2877.9\n", - "12 0.000033 1 build_real 2877.9\n", - "13 0.000034 1 prebuild_gwn 2877.9\n", - "14 0.000010 3 maxfactor 2877.4" + " function_name elapsed_time executions used_virtual_mem\n", + "0 Py_getvalues 0.000007 1 0.0\n", + "1 ran2 0.000065 41552 240.5\n", + "2 gasdev 0.000383 32768 240.5\n", + "3 generate 2.055015 1 219.2\n", + "4 maxfactor 0.000005 5 219.2\n", + "5 length 0.000071 3 219.2\n", + "6 cgrid 0.000265 1 219.2\n", + "7 cov_value 0.000156 24624 234.7\n", + "8 covariance 0.839737 1 228.0\n", + "9 fourt 0.003451 3 227.9\n", + "10 prebuild_gwn 0.000138 1 227.4\n", + "11 build_real 0.000424 1 227.4\n", + "12 clean_real 0.000158 1 227.4\n", + "13 fftma2 0.849271 1 227.4\n", + "14 Py_kgeneration 2.904626 1 227.4" ] }, - "execution_count": 54, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = {}\n", - "with open(\"log_8.txt\") as log_file:\n", + "with open(\"log_32.txt\") as log_file:\n", " lines = log_file.readlines()\n", " \n", " for line in lines:\n", @@ -216,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -225,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -249,169 +251,169 @@ " \n", " \n", " \n", + " function_name\n", " elapsed_time\n", " executions\n", - " function_name\n", " used_virtual_mem\n", " total_time\n", " \n", " \n", " \n", " \n", - " 1\n", - " 0.180264\n", - " 1\n", - " Py_kgeneration\n", - " 2877.9\n", - " 0.180264\n", + " 2\n", + " gasdev\n", + " 0.000383\n", + " 32768\n", + " 240.5\n", + " 12.550144\n", " \n", " \n", " 7\n", - " 0.100123\n", - " 1\n", - " generate\n", - " 2877.4\n", - " 0.100123\n", - " \n", - " \n", - " 6\n", - " 0.079166\n", - " 1\n", - " fftma2\n", - " 2877.9\n", - " 0.079166\n", + " cov_value\n", + " 0.000156\n", + " 24624\n", + " 234.7\n", + " 3.841344\n", " \n", " \n", - " 5\n", - " 0.077514\n", + " 14\n", + " Py_kgeneration\n", + " 2.904626\n", " 1\n", - " covariance\n", - " 2877.9\n", - " 0.077514\n", + " 227.4\n", + " 2.904626\n", " \n", " \n", - " 2\n", - " 0.001184\n", - " 512\n", - " gasdev\n", - " 2882.6\n", - " 0.606208\n", + " 1\n", + " ran2\n", + " 0.000065\n", + " 41552\n", + " 240.5\n", + " 2.700880\n", " \n", " \n", - " 0\n", - " 0.000456\n", + " 3\n", + " generate\n", + " 2.055015\n", " 1\n", - " cgrid\n", - " 2877.4\n", - " 0.000456\n", + " 219.2\n", + " 2.055015\n", " \n", " \n", - " 11\n", - " 0.000193\n", - " 3\n", - " fourt\n", - " 2877.9\n", - " 0.000579\n", + " 13\n", + " fftma2\n", + " 0.849271\n", + " 1\n", + " 227.4\n", + " 0.849271\n", " \n", " \n", " 8\n", - " 0.000098\n", - " 3\n", - " length\n", - " 2877.4\n", - " 0.000294\n", + " covariance\n", + " 0.839737\n", + " 1\n", + " 228.0\n", + " 0.839737\n", " \n", " \n", - " 3\n", - " 0.000081\n", - " 700\n", - " cov_value\n", - " 2877.9\n", - " 0.056700\n", + " 9\n", + " fourt\n", + " 0.003451\n", + " 3\n", + " 227.9\n", + " 0.010353\n", " \n", " \n", - " 10\n", - " 0.000037\n", - " 702\n", - " ran2\n", - " 2882.6\n", - " 0.025974\n", + " 11\n", + " build_real\n", + " 0.000424\n", + " 1\n", + " 227.4\n", + " 0.000424\n", " \n", " \n", - " 9\n", - " 0.000034\n", + " 6\n", + " cgrid\n", + " 0.000265\n", " 1\n", - " Py_getvalues\n", - " 0.0\n", - " 0.000034\n", + " 219.2\n", + " 0.000265\n", " \n", " \n", - " 13\n", - " 0.000034\n", - " 1\n", - " prebuild_gwn\n", - " 2877.9\n", - " 0.000034\n", + " 5\n", + " length\n", + " 0.000071\n", + " 3\n", + " 219.2\n", + " 0.000213\n", " \n", " \n", " 12\n", - " 0.000033\n", + " clean_real\n", + " 0.000158\n", " 1\n", - " build_real\n", - " 2877.9\n", - " 0.000033\n", + " 227.4\n", + " 0.000158\n", " \n", " \n", - " 4\n", - " 0.000015\n", + " 10\n", + " prebuild_gwn\n", + " 0.000138\n", " 1\n", - " clean_real\n", - " 2877.9\n", - " 0.000015\n", + " 227.4\n", + " 0.000138\n", " \n", " \n", - " 14\n", - " 0.000010\n", - " 3\n", + " 4\n", " maxfactor\n", - " 2877.4\n", - " 0.000030\n", + " 0.000005\n", + " 5\n", + " 219.2\n", + " 0.000025\n", + " \n", + " \n", + " 0\n", + " Py_getvalues\n", + " 0.000007\n", + " 1\n", + " 0.0\n", + " 0.000007\n", " \n", " \n", "\n", "" ], "text/plain": [ - " elapsed_time executions function_name used_virtual_mem total_time\n", - "1 0.180264 1 Py_kgeneration 2877.9 0.180264\n", - "7 0.100123 1 generate 2877.4 0.100123\n", - "6 0.079166 1 fftma2 2877.9 0.079166\n", - "5 0.077514 1 covariance 2877.9 0.077514\n", - "2 0.001184 512 gasdev 2882.6 0.606208\n", - "0 0.000456 1 cgrid 2877.4 0.000456\n", - "11 0.000193 3 fourt 2877.9 0.000579\n", - "8 0.000098 3 length 2877.4 0.000294\n", - "3 0.000081 700 cov_value 2877.9 0.056700\n", - "10 0.000037 702 ran2 2882.6 0.025974\n", - "9 0.000034 1 Py_getvalues 0.0 0.000034\n", - "13 0.000034 1 prebuild_gwn 2877.9 0.000034\n", - "12 0.000033 1 build_real 2877.9 0.000033\n", - "4 0.000015 1 clean_real 2877.9 0.000015\n", - "14 0.000010 3 maxfactor 2877.4 0.000030" + " function_name elapsed_time executions used_virtual_mem total_time\n", + "2 gasdev 0.000383 32768 240.5 12.550144\n", + "7 cov_value 0.000156 24624 234.7 3.841344\n", + "14 Py_kgeneration 2.904626 1 227.4 2.904626\n", + "1 ran2 0.000065 41552 240.5 2.700880\n", + "3 generate 2.055015 1 219.2 2.055015\n", + "13 fftma2 0.849271 1 227.4 0.849271\n", + "8 covariance 0.839737 1 228.0 0.839737\n", + "9 fourt 0.003451 3 227.9 0.010353\n", + "11 build_real 0.000424 1 227.4 0.000424\n", + "6 cgrid 0.000265 1 219.2 0.000265\n", + "5 length 0.000071 3 219.2 0.000213\n", + "12 clean_real 0.000158 1 227.4 0.000158\n", + "10 prebuild_gwn 0.000138 1 227.4 0.000138\n", + "4 maxfactor 0.000005 5 219.2 0.000025\n", + "0 Py_getvalues 0.000007 1 0.0 0.000007" ] }, - "execution_count": 56, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.sort_values(by=[\"elapsed_time\"], ascending=False)" + "df.sort_values(by=[\"total_time\", \"used_virtual_mem\"], ascending=False)" ] }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -435,355 +437,169 @@ " \n", " \n", " \n", + " function_name\n", " elapsed_time\n", " executions\n", - " function_name\n", " used_virtual_mem\n", " total_time\n", " \n", " \n", " \n", " \n", + " 1\n", + " ran2\n", + " 0.000220\n", + " 333450\n", + " 548.2\n", + " 73.359000\n", + " \n", + " \n", " 2\n", - " 0.001184\n", - " 512\n", " gasdev\n", - " 2882.6\n", - " 0.606208\n", + " 0.000707\n", + " 262144\n", + " 548.2\n", + " 185.335808\n", " \n", " \n", - " 1\n", - " 0.180264\n", + " 3\n", + " generate\n", + " 20.687490\n", " 1\n", - " Py_kgeneration\n", - " 2877.9\n", - " 0.180264\n", + " 425.6\n", + " 20.687490\n", " \n", " \n", - " 7\n", - " 0.100123\n", - " 1\n", - " generate\n", - " 2877.4\n", - " 0.100123\n", + " 4\n", + " maxfactor\n", + " 0.000005\n", + " 4\n", + " 425.6\n", + " 0.000020\n", " \n", " \n", - " 6\n", - " 0.079166\n", - " 1\n", - " fftma2\n", - " 2877.9\n", - " 0.079166\n", + " 5\n", + " length\n", + " 0.000059\n", + " 3\n", + " 425.6\n", + " 0.000177\n", " \n", " \n", - " 5\n", - " 0.077514\n", + " 6\n", + " cgrid\n", + " 0.000199\n", " 1\n", - " covariance\n", - " 2877.9\n", - " 0.077514\n", + " 425.6\n", + " 0.000199\n", " \n", " \n", - " 3\n", - " 0.000081\n", - " 700\n", + " 7\n", " cov_value\n", - " 2877.9\n", - " 0.056700\n", + " 0.000158\n", + " 156816\n", + " 425.6\n", + " 24.776928\n", " \n", " \n", - " 10\n", - " 0.000037\n", - " 702\n", - " ran2\n", - " 2882.6\n", - " 0.025974\n", + " 8\n", + " covariance\n", + " 7.813795\n", + " 1\n", + " 313.0\n", + " 7.813795\n", " \n", " \n", - " 11\n", - " 0.000193\n", - " 3\n", - " fourt\n", - " 2877.9\n", - " 0.000579\n", + " 12\n", + " clean_real\n", + " 0.000966\n", + " 1\n", + " 311.3\n", + " 0.000966\n", " \n", " \n", - " 0\n", - " 0.000456\n", + " 14\n", + " Py_kgeneration\n", + " 28.676072\n", " 1\n", - " cgrid\n", - " 2877.4\n", - " 0.000456\n", + " 311.3\n", + " 28.676072\n", " \n", " \n", - " 8\n", - " 0.000098\n", + " 9\n", + " fourt\n", + " 0.069088\n", " 3\n", - " length\n", - " 2877.4\n", - " 0.000294\n", + " 311.1\n", + " 0.207264\n", " \n", " \n", - " 9\n", - " 0.000034\n", + " 10\n", + " prebuild_gwn\n", + " 0.001624\n", " 1\n", - " Py_getvalues\n", - " 0.0\n", - " 0.000034\n", + " 308.7\n", + " 0.001624\n", " \n", " \n", - " 13\n", - " 0.000034\n", + " 11\n", + " build_real\n", + " 0.004294\n", " 1\n", - " prebuild_gwn\n", - " 2877.9\n", - " 0.000034\n", + " 308.4\n", + " 0.004294\n", " \n", " \n", - " 12\n", - " 0.000033\n", + " 13\n", + " fftma2\n", + " 7.988212\n", " 1\n", - " build_real\n", - " 2877.9\n", - " 0.000033\n", + " 308.4\n", + " 7.988212\n", " \n", " \n", - " 14\n", - " 0.000010\n", - " 3\n", - " maxfactor\n", - " 2877.4\n", - " 0.000030\n", - " \n", - " \n", - " 4\n", - " 0.000015\n", + " 0\n", + " Py_getvalues\n", + " 0.000009\n", " 1\n", - " clean_real\n", - " 2877.9\n", - " 0.000015\n", + " 0.0\n", + " 0.000009\n", " \n", " \n", "\n", "" ], "text/plain": [ - " elapsed_time executions function_name used_virtual_mem total_time\n", - "2 0.001184 512 gasdev 2882.6 0.606208\n", - "1 0.180264 1 Py_kgeneration 2877.9 0.180264\n", - "7 0.100123 1 generate 2877.4 0.100123\n", - "6 0.079166 1 fftma2 2877.9 0.079166\n", - "5 0.077514 1 covariance 2877.9 0.077514\n", - "3 0.000081 700 cov_value 2877.9 0.056700\n", - "10 0.000037 702 ran2 2882.6 0.025974\n", - "11 0.000193 3 fourt 2877.9 0.000579\n", - "0 0.000456 1 cgrid 2877.4 0.000456\n", - "8 0.000098 3 length 2877.4 0.000294\n", - "9 0.000034 1 Py_getvalues 0.0 0.000034\n", - "13 0.000034 1 prebuild_gwn 2877.9 0.000034\n", - "12 0.000033 1 build_real 2877.9 0.000033\n", - "14 0.000010 3 maxfactor 2877.4 0.000030\n", - "4 0.000015 1 clean_real 2877.9 0.000015" + " function_name elapsed_time executions used_virtual_mem total_time\n", + "1 ran2 0.000220 333450 548.2 73.359000\n", + "2 gasdev 0.000707 262144 548.2 185.335808\n", + "3 generate 20.687490 1 425.6 20.687490\n", + "4 maxfactor 0.000005 4 425.6 0.000020\n", + "5 length 0.000059 3 425.6 0.000177\n", + "6 cgrid 0.000199 1 425.6 0.000199\n", + "7 cov_value 0.000158 156816 425.6 24.776928\n", + "8 covariance 7.813795 1 313.0 7.813795\n", + "12 clean_real 0.000966 1 311.3 0.000966\n", + "14 Py_kgeneration 28.676072 1 311.3 28.676072\n", + "9 fourt 0.069088 3 311.1 0.207264\n", + "10 prebuild_gwn 0.001624 1 308.7 0.001624\n", + "11 build_real 0.004294 1 308.4 0.004294\n", + "13 fftma2 7.988212 1 308.4 7.988212\n", + "0 Py_getvalues 0.000009 1 0.0 0.000009" ] }, - "execution_count": 57, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.sort_values(by=[\"total_time\"], ascending=False)" + "df.sort_values(by=[\"used_virtual_mem\"], ascending=False)" ] }, { "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
elapsed_timeexecutionsfunction_nameused_virtual_memtotal_time
20.001184512gasdev2882.60.606208
100.000037702ran22882.60.025974
10.1802641Py_kgeneration2877.90.180264
30.000081700cov_value2877.90.056700
40.0000151clean_real2877.90.000015
50.0775141covariance2877.90.077514
60.0791661fftma22877.90.079166
110.0001933fourt2877.90.000579
120.0000331build_real2877.90.000033
130.0000341prebuild_gwn2877.90.000034
00.0004561cgrid2877.40.000456
70.1001231generate2877.40.100123
80.0000983length2877.40.000294
140.0000103maxfactor2877.40.000030
90.0000341Py_getvalues0.00.000034
\n", - "
" - ], - "text/plain": [ - " elapsed_time executions function_name used_virtual_mem total_time\n", - "2 0.001184 512 gasdev 2882.6 0.606208\n", - "10 0.000037 702 ran2 2882.6 0.025974\n", - "1 0.180264 1 Py_kgeneration 2877.9 0.180264\n", - "3 0.000081 700 cov_value 2877.9 0.056700\n", - "4 0.000015 1 clean_real 2877.9 0.000015\n", - "5 0.077514 1 covariance 2877.9 0.077514\n", - "6 0.079166 1 fftma2 2877.9 0.079166\n", - "11 0.000193 3 fourt 2877.9 0.000579\n", - "12 0.000033 1 build_real 2877.9 0.000033\n", - "13 0.000034 1 prebuild_gwn 2877.9 0.000034\n", - "0 0.000456 1 cgrid 2877.4 0.000456\n", - "7 0.100123 1 generate 2877.4 0.100123\n", - "8 0.000098 3 length 2877.4 0.000294\n", - "14 0.000010 3 maxfactor 2877.4 0.000030\n", - "9 0.000034 1 Py_getvalues 0.0 0.000034" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.sort_values(by=[\"used_virtual_mem\"], ascending=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -810,36 +626,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "\n", - "plt.figure(dpi=125)\n", - "set_a = set([0.023829, 0.010957, 0.012503])\n", - "\n", - "set_b = set([0.010957])\n", - "\n", - "set_c = set([0.012503])\n", - "\n", - "venn3([set_a, set_b, set_c], ('Py_kgeneration', 'generate', 'fftma2'))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "venn3([set(['Py_kgeneration', 'generate', 'fftma2']), set(['generate', 'fftma2']), set(['fftma2'])])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt" - ] + "source": [] }, { "cell_type": "code", @@ -916,6 +703,427 @@ "plt.show()\n" ] }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "data = []\n", + "with open(\"log_64.txt\") as log_file:\n", + " lines = log_file.readlines()\n", + " \n", + " for line in lines:\n", + " row = {}\n", + " if \"MEM\" in line:\n", + " split_line = line.split()\n", + " idx_used_mem = split_line.index(\"USED\") + 4\n", + " function_name, used_virtual_mem = get_function_name(split_line[2]), float(split_line[idx_used_mem])\n", + " #val = data.get(function_name, (0, 0, used_virtual_mem))\n", + " #data[function_name] = (val[0], val[1], max(used_virtual_mem, val[2]))\n", + "\n", + " row[\"function\"] = function_name\n", + " row[\"memory\"] = used_virtual_mem \n", + "\n", + " if \"ELAPSED\" in line:\n", + " split_line = line.split()\n", + " idx_elapsed = split_line.index(\"ELAPSED\") + 2\n", + " function_name, elapsed = get_function_name(split_line[2]), float(split_line[idx_elapsed].rsplit(\",\")[0])\n", + " #val = data.get(function_name, (elapsed, 0, 0))\n", + " #data[function_name] = (max(elapsed, val[0]), val[1] + 1, val[2])\n", + " #data.append({\"function\": function_name, \"time\": elapsed, \"memory\": 0})\n", + " row[\"function\"] = function_name\n", + " row[\"time\"] = elapsed\n", + "\n", + " if row: data.append(row)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "df2 = pd.DataFrame(data) " + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "df2 = df2.fillna(np.nan)" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
functiontimememory
0Py_getvalues0.000008NaN
1ran20.0000030.0
2ran20.0000230.0
3gasdev0.0000460.0
4gasdev0.0000060.0
\n", + "
" + ], + "text/plain": [ + " function time memory\n", + "0 Py_getvalues 0.000008 NaN\n", + "1 ran2 0.000003 0.0\n", + "2 ran2 0.000023 0.0\n", + "3 gasdev 0.000046 0.0\n", + "4 gasdev 0.000006 0.0" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "df2_grouped = df2.groupby(['function']).agg({'time': ['min', 'max', 'mean', 'sum', 'count'], 'memory': ['min', 'max', 'median']})" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
timememory
minmaxmeansumcountminmaxmedian
function
Py_kgeneration11.31662111.31662111.31662111.3166211-293.7-293.7-293.7
generate7.4248317.4248317.4248317.4248311-226.3-226.3-226.3
gasdev0.0000030.0002770.0000215.484581262144-20.05.30.0
fftma23.8914313.8914313.8914313.8914311-69.5-69.5-69.5
covariance3.7657313.7657313.7657313.7657311-65.0-65.0-65.0
cov_value0.0000070.0001080.0000132.062640156816-1.91.50.0
ran20.0000020.0001140.0000051.534732333450-19.72.60.0
fourt0.0327780.0515450.0396640.1189913-1.9-0.0-0.2
build_real0.0040530.0040530.0040530.00405310.00.00.0
prebuild_gwn0.0011320.0011320.0011320.0011321-2.5-2.5-2.5
clean_real0.0008300.0008300.0008300.0008301-0.7-0.7-0.7
cgrid0.0001480.0001480.0001480.00014810.00.00.0
length0.0000150.0000640.0000390.00011830.00.00.0
maxfactor0.0000040.0000250.0000110.00004340.00.00.0
Py_getvalues0.0000080.0000080.0000080.0000081NaNNaNNaN
\n", + "
" + ], + "text/plain": [ + " time memory \\\n", + " min max mean sum count min \n", + "function \n", + "Py_kgeneration 11.316621 11.316621 11.316621 11.316621 1 -293.7 \n", + "generate 7.424831 7.424831 7.424831 7.424831 1 -226.3 \n", + "gasdev 0.000003 0.000277 0.000021 5.484581 262144 -20.0 \n", + "fftma2 3.891431 3.891431 3.891431 3.891431 1 -69.5 \n", + "covariance 3.765731 3.765731 3.765731 3.765731 1 -65.0 \n", + "cov_value 0.000007 0.000108 0.000013 2.062640 156816 -1.9 \n", + "ran2 0.000002 0.000114 0.000005 1.534732 333450 -19.7 \n", + "fourt 0.032778 0.051545 0.039664 0.118991 3 -1.9 \n", + "build_real 0.004053 0.004053 0.004053 0.004053 1 0.0 \n", + "prebuild_gwn 0.001132 0.001132 0.001132 0.001132 1 -2.5 \n", + "clean_real 0.000830 0.000830 0.000830 0.000830 1 -0.7 \n", + "cgrid 0.000148 0.000148 0.000148 0.000148 1 0.0 \n", + "length 0.000015 0.000064 0.000039 0.000118 3 0.0 \n", + "maxfactor 0.000004 0.000025 0.000011 0.000043 4 0.0 \n", + "Py_getvalues 0.000008 0.000008 0.000008 0.000008 1 NaN \n", + "\n", + " \n", + " max median \n", + "function \n", + "Py_kgeneration -293.7 -293.7 \n", + "generate -226.3 -226.3 \n", + "gasdev 5.3 0.0 \n", + "fftma2 -69.5 -69.5 \n", + "covariance -65.0 -65.0 \n", + "cov_value 1.5 0.0 \n", + "ran2 2.6 0.0 \n", + "fourt -0.0 -0.2 \n", + "build_real 0.0 0.0 \n", + "prebuild_gwn -2.5 -2.5 \n", + "clean_real -0.7 -0.7 \n", + "cgrid 0.0 0.0 \n", + "length 0.0 0.0 \n", + "maxfactor 0.0 0.0 \n", + "Py_getvalues NaN NaN " + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2_grouped.sort_values(by=('time', 'sum'), ascending=False)" + ] + }, { "cell_type": "code", "execution_count": null,