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Commit a3730724 authored by iker_martin's avatar iker_martin
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Merge branch 'dev' of https://gitlab.com/iker_martin/malleability_benchmark into dev

parents eed0d373 ac8406db
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"from pandas import DataFrame, Series\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"from scipy import stats\n",
"import sys"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"matrixMalEX=\"data_GG.csv\"\n",
"matrixMal=\"data_GM.csv\"\n",
"matrixIt=\"data_L.csv\"\n",
"n_qty=2 #CAMBIAR SEGUN LA CANTIDAD DE NODOS USADOS\n",
"repet = 3 * 2 #CAMBIAR EL PRIMER NUMERO SEGUN NUMERO DE EJECUCIONES POR CONFIG\n",
"\n",
"p_value = 0.05\n",
"values = [2, 4, 8, 16, 32]\n",
"dist_names = ['null', 'BestFit', 'WorstFit']"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def speedUp(arr, seq, df):\n",
" numP = df.loc[arr.index[0]].NP\n",
" return seq[( seq.NP == numP )]['EX'] / arr.mean()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"dfG = pd.read_csv( matrixMalEX )\n",
"\n",
"dfG = dfG.drop(columns=dfG.columns[0])\n",
"dfG['S'] = dfG['N']\n",
"dfG['N'] = dfG['S'] + dfG['%Async']\n",
"dfG['%Async'] = (dfG['%Async'] / dfG['N']) * 100\n",
"\n",
"if(n_qty == 1):\n",
" group = dfG.groupby(['%Async', 'Groups'])['TE']\n",
"else: \n",
" group = dfG.groupby(['Dist', '%Async', 'Groups'])['TE']\n",
"\n",
"#group\n",
"grouped_aggG = group.agg(['mean'])\n",
"grouped_aggG.rename(columns={'mean':'TE',}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from natsort import index_natsorted\n",
"grouped_aggG.sort_values(\n",
" by=\"Groups\",\n",
" key=lambda x: np.argsort(index_natsorted(df[\"Groups\"]))\n",
")\n",
"grouped_aggG"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"dfM = pd.read_csv( matrixMal )\n",
"dfM = dfM.drop(columns=dfM.columns[0])\n",
"\n",
"dfM['S'] = dfM['N']\n",
"dfM['N'] = dfM['S'] + dfM['%Async']\n",
"dfM[\"TR\"] = dfM[\"TC\"] + dfM[\"TS\"] + dfM[\"TA\"]\n",
"dfM['%Async'] = (dfM['%Async'] / dfM['N']) * 100\n",
"\n",
"if(n_qty == 1):\n",
" groupM = dfM.groupby(['%Async','NP', 'NS'])['TC', 'TS', 'TA', 'TR']\n",
"else:\n",
" groupM = dfM.groupby(['Dist', '%Async','NP', 'NS'])['TC', 'TS', 'TA', 'TR']\n",
"\n",
"#group\n",
"grouped_aggM = groupM.agg(['mean'])\n",
"grouped_aggM.columns = grouped_aggM.columns.get_level_values(0)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:16: FutureWarning: set_axis currently defaults to operating inplace.\n",
"This will change in a future version of pandas, use inplace=True to avoid this warning.\n",
" app.launch_new_instance()\n"
]
}
],
"source": [
"dfL = pd.read_csv( matrixIt )\n",
"dfL = dfL.drop(columns=dfL.columns[0])\n",
"\n",
"dfL['S'] = dfL['N']\n",
"dfL['N'] = dfL['S'] + dfL['%Async']\n",
"dfL['%Async'] = (dfL['%Async'] / dfL['N']) * 100\n",
"\n",
"if(n_qty == 1):\n",
" groupL = dfL[dfL['NS'] != 0].groupby(['Tt', '%Async', 'NP', 'NS'])['Ti', 'To']\n",
"else:\n",
" groupL = dfL[dfL['NS'] != 0].groupby(['Tt', 'Dist', '%Async', 'NP', 'NS'])['Ti', 'To']\n",
"\n",
"#group\n",
"grouped_aggL = groupL.agg(['mean', 'count'])\n",
"grouped_aggL.columns = grouped_aggL.columns.get_level_values(0)\n",
"grouped_aggL.set_axis(['Ti', 'Iters', 'To', 'Iters2'], axis='columns')\n",
"\n",
"grouped_aggL['Iters'] = np.ceil(grouped_aggL['Iters']/6) # TODO Cambiar a repeticiones realizadas\n",
"grouped_aggL['Iters2'] = np.ceil(grouped_aggL['Iters2']/6)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"grouped_aggL.to_excel(\"resultL.xlsx\") \n",
"grouped_aggM.to_excel(\"resultM.xlsx\") \n",
"grouped_aggG.to_excel(\"resultG.xlsx\") "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
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"<p>600 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
" N %Async Groups Dist Matrix Time Iters TE \\\n",
"0 1000000000 0.0 8,32 2,2 100000 0.1 1000 31.525710 \n",
"1 1000000000 0.0 8,32 2,2 100000 0.1 1000 33.315857 \n",
"2 1000000000 0.0 8,32 2,2 100000 0.1 1000 33.347537 \n",
"3 1000000000 75.0 4,32 1,1 100000 0.1 1000 57.219027 \n",
"4 1000000000 75.0 4,32 1,1 100000 0.1 1000 55.469166 \n",
".. ... ... ... ... ... ... ... ... \n",
"595 1000000000 50.0 4,8 2,2 100000 0.1 1000 73.771357 \n",
"596 1000000000 50.0 4,8 2,2 100000 0.1 1000 75.557508 \n",
"597 1000000000 50.0 16,8 1,1 100000 0.1 1000 35.949913 \n",
"598 1000000000 50.0 16,8 1,1 100000 0.1 1000 37.900348 \n",
"599 1000000000 50.0 16,8 1,1 100000 0.1 1000 36.012772 \n",
"\n",
" S \n",
"0 1000000000 \n",
"1 1000000000 \n",
"2 1000000000 \n",
"3 250000000 \n",
"4 250000000 \n",
".. ... \n",
"595 500000000 \n",
"596 500000000 \n",
"597 500000000 \n",
"598 500000000 \n",
"599 500000000 \n",
"\n",
"[600 rows x 9 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfG"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
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" TE\n",
"Dist %Async Groups \n",
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" 16,4 61.887052\n",
" 16,8 37.986463\n",
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"\n",
"[200 rows x 1 columns]"
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},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grouped_aggG"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
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" <th>N</th>\n",
" <th>%Async</th>\n",
" <th>NP</th>\n",
" <th>NS</th>\n",
" <th>Dist</th>\n",
" <th>Matrix</th>\n",
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" N %Async NP NS Dist Matrix Time Iters TC TS \\\n",
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"1 1000000000 0.0 8 32 2,2 100000 0.1 1000 1.405911 0.639849 \n",
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"3 1000000000 75.0 4 32 1,1 100000 0.1 1000 0.688062 0.064900 \n",
"4 1000000000 75.0 4 32 1,1 100000 0.1 1000 0.702200 0.068039 \n",
".. ... ... .. .. ... ... ... ... ... ... \n",
"595 1000000000 50.0 4 8 2,2 100000 0.1 1000 0.390444 0.142287 \n",
"596 1000000000 50.0 4 8 2,2 100000 0.1 1000 0.351690 0.154699 \n",
"597 1000000000 50.0 16 8 1,1 100000 0.1 1000 0.337472 0.081077 \n",
"598 1000000000 50.0 16 8 1,1 100000 0.1 1000 0.351262 0.103176 \n",
"599 1000000000 50.0 16 8 1,1 100000 0.1 1000 0.349151 0.094121 \n",
"\n",
" TA S TR \n",
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"4 0.412072 250000000 1.182311 \n",
".. ... ... ... \n",
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"596 0.354917 500000000 0.861306 \n",
"597 0.243481 500000000 0.662030 \n",
"598 0.261565 500000000 0.716003 \n",
"599 0.221750 500000000 0.665022 \n",
"\n",
"[600 rows x 13 columns]"
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},
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"metadata": {},
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],
"source": [
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"text/plain": [
" TC TS TA TR\n",
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"[200 rows x 4 columns]"
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},
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"text/plain": [
" N %Async NP N_par NS Dist Matrix Time Iters \\\n",
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"2 1000000000 0.0 32 8 0 2 100000 0.1 1000 \n",
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"4 1000000000 0.0 32 8 0 2 100000 0.1 1000 \n",
"... ... ... .. ... .. ... ... ... ... \n",
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"1199996 1000000000 50.0 8 16 0 1 100000 0.1 1000 \n",
"1199997 1000000000 50.0 8 16 0 1 100000 0.1 1000 \n",
"1199998 1000000000 50.0 8 16 0 1 100000 0.1 1000 \n",
"1199999 1000000000 50.0 8 16 0 1 100000 0.1 1000 \n",
"\n",
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"4 0.005352 0.0 6.0 1000000000 \n",
"... ... ... ... ... \n",
"1199995 0.024075 0.0 27.0 500000000 \n",
"1199996 0.024076 0.0 27.0 500000000 \n",
"1199997 0.024076 0.0 27.0 500000000 \n",
"1199998 0.024411 0.0 27.0 500000000 \n",
"1199999 0.024075 0.0 27.0 500000000 \n",
"\n",
"[1200000 rows x 13 columns]"
]
},
"execution_count": 12,
"metadata": {},
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}
],
"source": [
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{
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"execution_count": 13,
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"text/plain": [
" Ti Iters To Iters2\n",
"Tt Dist %Async NP NS \n",
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"... ... ... ... ...\n",
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" 32 2 0.012084 101.0 7.000000 101.0\n",
" 4 0.015599 54.0 7.000000 54.0\n",
" 8 0.012685 54.0 7.000000 54.0\n",
" 16 0.015421 44.0 7.000000 44.0\n",
"\n",
"[360 rows x 4 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grouped_aggL"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TIEMPO EJECUCCION\n",
"Distribución BestFit -------------------------\n",
"Para 2 padres\n",
"EX numC= 4 p = 0.039 Diff = 0.09 Asíncrono\n",
"Para 4 padres\n",
"Para 8 padres\n",
"EX numC= 4 p = 0.0 Diff = 1.411 Síncrono\n",
"Para 16 padres\n",
"EX numC= 2 p = 0.014 Diff = 3.662 Asíncrono\n",
"Para 32 padres\n",
"EX numC= 4 p = 0.002 Diff = 4.589 Asíncrono\n",
"Distribución WorstFit -------------------------\n",
"Para 2 padres\n",
"Para 4 padres\n",
"EX numC= 16 p = 0.046 Diff = 0.304 Síncrono\n",
"EX numC= 32 p = 0.012 Diff = 0.542 Síncrono\n",
"Para 8 padres\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
" # Remove the CWD from sys.path while we load stuff.\n",
"/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
" # This is added back by InteractiveShellApp.init_path()\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Para 16 padres\n",
"EX numC= 2 p = 0.023 Diff = 6.349 Asíncrono\n",
"EX numC= 4 p = 0.0 Diff = 1.799 Asíncrono\n",
"EX numC= 8 p = 0.046 Diff = 0.261 Asíncrono\n",
"Para 32 padres\n",
"EX numC= 2 p = 0.01 Diff = 18.514 Asíncrono\n",
"EX numC= 4 p = 0.0 Diff = 4.454 Asíncrono\n",
"EX numC= 8 p = 0.003 Diff = 1.92 Asíncrono\n"
]
}
],
"source": [
"print(\"TIEMPO EJECUCCION\")\n",
"for dist in [1,2]:\n",
" print(\"Distribución \" + dist_names[dist] + \" -------------------------\")\n",
" dist_v = str(dist)+\",\"+str(dist)\n",
" for numP in values:\n",
" print(\"Para \", numP, \" padres\")\n",
" for numC in values:\n",
" if numP != numC:\n",
" group = str(numP) + \",\" + str(numC)\n",
" v1 = dfG[(dfG[\"%Async\"] == 0.0)][(dfG.Groups == group)][(dfG[\"Dist\"] == dist_v)]['TE']\n",
" v2 = dfG[(dfG[\"%Async\"] == 100.0)][(dfG.Groups == group)][(dfG[\"Dist\"] == dist_v)]['TE']\n",
" res = stats.ttest_ind(v1, v2)\n",
" diff = grouped_aggG['TE'].loc[(dist_v, 0.0, group)] - grouped_aggG['TE'].loc[(dist_v, 100.0, group)]\n",
" if diff > 0:\n",
" mejor = \"Asíncrono\"\n",
" else:\n",
" mejor = \"Síncrono\"\n",
" \n",
" if res[1] < p_value:\n",
" print(\"EX numC=\", numC, \"p =\", round(res[1],3), \"Diff =\", abs(round(diff,3)), mejor)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TIEMPO MALLEABILITY\n",
"Distribución 1 -------------------------\n",
"Para 2 padres\n",
"TR numC= 8 p = 0.0 Diff = 0.029 Síncrono\n",
"Para 4 padres\n",
"TR numC= 8 p = 0.006 Diff = 0.043 Síncrono\n",
"TR numC= 32 p = 0.025 Diff = 0.052 Síncrono\n",
"Para 8 padres\n",
"TR numC= 4 p = 0.016 Diff = 0.022 Asíncrono\n",
"TR numC= 16 p = 0.009 Diff = 0.031 Síncrono\n",
"TR numC= 32 p = 0.0 Diff = 0.483 Síncrono\n",
"Para 16 padres\n",
"TR numC= 8 p = 0.002 Diff = 0.072 Síncrono\n",
"TR numC= 32 p = 0.019 Diff = 0.549 Síncrono\n",
"Para 32 padres\n",
"TR numC= 4 p = 0.002 Diff = 0.981 Síncrono\n",
"TR numC= 8 p = 0.001 Diff = 0.972 Síncrono\n",
"TR numC= 16 p = 0.001 Diff = 0.831 Síncrono\n",
"Distribución 2 -------------------------\n",
"Para 2 padres\n",
"TR numC= 4 p = 0.0 Diff = 0.069 Síncrono\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:9: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
" if __name__ == '__main__':\n",
"/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
" # Remove the CWD from sys.path while we load stuff.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"TR numC= 8 p = 0.029 Diff = 0.035 Síncrono\n",
"Para 4 padres\n",
"TR numC= 8 p = 0.02 Diff = 0.031 Síncrono\n",
"TR numC= 32 p = 0.009 Diff = 0.601 Síncrono\n",
"Para 8 padres\n",
"TR numC= 16 p = 0.011 Diff = 0.729 Síncrono\n",
"TR numC= 32 p = 0.018 Diff = 0.423 Síncrono\n",
"Para 16 padres\n",
"TR numC= 4 p = 0.001 Diff = 0.884 Síncrono\n",
"TR numC= 32 p = 0.021 Diff = 0.36 Síncrono\n",
"Para 32 padres\n",
"TR numC= 2 p = 0.029 Diff = 1.548 Síncrono\n",
"TR numC= 4 p = 0.008 Diff = 0.84 Síncrono\n",
"TR numC= 8 p = 0.011 Diff = 0.726 Síncrono\n",
"TR numC= 16 p = 0.004 Diff = 0.606 Síncrono\n"
]
}
],
"source": [
"print(\"TIEMPO MALLEABILITY\")\n",
"for dist in [1,2]:\n",
" print(\"Distribución \" + dist_names[dist] + \" -------------------------\")\n",
" dist_v = str(dist)+\",\"+str(dist)\n",
" for numP in values:\n",
" print(\"Para \", numP, \" padres\")\n",
" for numC in values:\n",
" if numP != numC:\n",
" v1 = dfM[(dfM[\"%Async\"] == 0.0)][(dfM.NP == numP)][(dfM.NS == numC)][(dfM[\"Dist\"] == dist_v)]['TS']\n",
" v2 = dfM[(dfM[\"%Async\"] == 100.0)][(dfM.NP == numP)][(dfM.NS == numC)][(dfM[\"Dist\"] == dist_v)]['TA']\n",
" res = stats.ttest_ind(v1, v2)\n",
" diff = grouped_aggM['TS'].loc[(dist_v, 0.0, numP, numC)] - grouped_aggM['TA'].loc[(dist_v, 100.0, numP, numC)]\n",
" if diff > 0:\n",
" mejor = \"Asíncrono\"\n",
" else:\n",
" mejor = \"Síncrono\"\n",
" if res[1] < p_value:\n",
" print(\"TR numC=\", numC, \"p =\", round(res[1],3), \"Diff =\", abs(round(diff,3)), mejor)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TIEMPO Iters\n",
"Distribución BestFit -------------------------\n",
"Para 2 padres\n",
"Ti numC= 4 p = 0.035 Diff = 0.0001 Síncrono\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
" if sys.path[0] == '':\n",
"/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
" del sys.path[0]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ti numC= 8 p = 0.025 Diff = 0.0001 Síncrono\n",
"Ti numC= 16 p = 0.002 Diff = 0.0001 Síncrono\n",
"Ti numC= 32 p = 0.007 Diff = 0.0001 Síncrono\n",
"Para 4 padres\n",
"Ti numC= 16 p = 0.0 Diff = 0.0006 Síncrono\n",
"Para 8 padres\n",
"Ti numC= 4 p = 0.0 Diff = 0.0009 Síncrono\n",
"Ti numC= 32 p = 0.007 Diff = 0.0029 Síncrono\n",
"Para 16 padres\n",
"Ti numC= 32 p = 0.0 Diff = 0.0097 Síncrono\n",
"Para 32 padres\n",
"Ti numC= 2 p = 0.0 Diff = 0.0029 Síncrono\n",
"Ti numC= 4 p = 0.0 Diff = 0.0056 Síncrono\n",
"Ti numC= 8 p = 0.0 Diff = 0.0055 Síncrono\n",
"Ti numC= 16 p = 0.0 Diff = 0.0053 Síncrono\n",
"Distribución WorstFit -------------------------\n",
"Para 2 padres\n",
"Ti numC= 32 p = 0.024 Diff = 0.0256 Síncrono\n",
"Para 4 padres\n",
"Ti numC= 16 p = 0.0 Diff = 0.0243 Síncrono\n",
"Ti numC= 32 p = 0.0 Diff = 0.0343 Síncrono\n",
"Para 8 padres\n",
"Ti numC= 4 p = 0.036 Diff = 0.0044 Síncrono\n",
"Ti numC= 16 p = 0.0 Diff = 0.008 Síncrono\n",
"Ti numC= 32 p = 0.0 Diff = 0.0225 Síncrono\n",
"Para 16 padres\n",
"Ti numC= 2 p = 0.0 Diff = 0.0067 Síncrono\n",
"Ti numC= 4 p = 0.0 Diff = 0.0092 Síncrono\n",
"Ti numC= 8 p = 0.0 Diff = 0.0098 Síncrono\n",
"Ti numC= 32 p = 0.0 Diff = 0.0224 Síncrono\n",
"Para 32 padres\n",
"Ti numC= 2 p = 0.0 Diff = 0.0058 Síncrono\n",
"Ti numC= 4 p = 0.0 Diff = 0.0094 Síncrono\n",
"Ti numC= 8 p = 0.0 Diff = 0.0064 Síncrono\n",
"Ti numC= 16 p = 0.0 Diff = 0.0092 Síncrono\n"
]
}
],
"source": [
"print(\"TIEMPO Iters\")\n",
"for dist in [1,2]:\n",
" print(\"Distribución \" + dist_names[dist] + \" -------------------------\")\n",
" dist_v = str(dist)+\",\"+str(dist)\n",
" for numP in values:\n",
" print(\"Para \", numP, \" padres\")\n",
" for numC in values:\n",
" if numP != numC:\n",
" #exp = dfL[(dfL[\"Tt\"] == 0)][(dfL[\"Dist\"] == 1)][(dfL[\"%Async\"] == 0.0)][(dfL.NP == numP)][(dfL.NS == numC)]\n",
" #TimeOp = exp['Ti'] \n",
" #print(TimeOp)\n",
" v1 = dfL[(dfL[\"Tt\"] == 0)][(dfL[\"Dist\"] == dist)][(dfL[\"%Async\"] == 100.0)][(dfL.NP == numP)][(dfL.NS == numC)]['Ti']\n",
" v2 = dfL[(dfL[\"Tt\"] == 1)][(dfL[\"Dist\"] == dist)][(dfL[\"%Async\"] == 100.0)][(dfL.NP == numP)][(dfL.NS == numC)]['Ti']\n",
" res = stats.ttest_ind(v1, v2, equal_var = False)\n",
" diff = grouped_aggL['Ti'].loc[(0, dist, 0.0, numP, numC)] - grouped_aggL['Ti'].loc[(1, dist, 100.0, numP, numC)]\n",
" if diff > 0:\n",
" mejor = \"Asíncrono\"\n",
" else:\n",
" mejor = \"Síncrono\"\n",
" if res[1] < p_value:\n",
" #and abs(diff) > grouped_aggL['Ti'].loc[(0, dist, 0.0, numP, numC)]\n",
" print(\"Ti numC=\", numC, \"p =\", round(res[1],3), \"Diff =\", abs(round(diff,4)), mejor)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Distribución BestFit -------------------------\n",
"Para 2 padres\n",
"NC=4 Es mejor Asíncrono con una diff de 0.123\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
" # Remove the CWD from sys.path while we load stuff.\n",
"/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
" \n",
"/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:16: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
" app.launch_new_instance()\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"NC=8 Es mejor Asíncrono con una diff de 0.07\n",
"NC=16 Es mejor Asíncrono con una diff de 0.046\n",
"NC=32 Es mejor Asíncrono con una diff de 0.014\n",
"Para 4 padres\n",
"NC=2 Es mejor Asíncrono con una diff de 0.645\n",
"NC=8 Es mejor Asíncrono con una diff de 0.13\n",
"NC=16 Es mejor Asíncrono con una diff de 0.052\n",
"NC=32 Es mejor Síncrono con una diff de 0.005\n",
"Para 8 padres\n",
"NC=2 Es mejor Asíncrono con una diff de 1.523\n",
"NC=4 Es mejor Asíncrono con una diff de 0.354\n",
"NC=16 Es mejor Asíncrono con una diff de 0.066\n",
"NC=32 Es mejor Síncrono con una diff de 0.327\n",
"Para 16 padres\n",
"NC=2 Es mejor Asíncrono con una diff de 3.676\n",
"NC=4 Es mejor Asíncrono con una diff de 1.235\n",
"NC=8 Es mejor Asíncrono con una diff de 0.406\n",
"NC=32 Es mejor Síncrono con una diff de 0.304\n",
"Para 32 padres\n",
"NC=2 Es mejor Asíncrono con una diff de 16.171\n",
"NC=4 Es mejor Asíncrono con una diff de 4.551\n",
"NC=8 Es mejor Asíncrono con una diff de 1.599\n",
"NC=16 Es mejor Asíncrono con una diff de 0.12\n",
"Distribución WorstFit -------------------------\n",
"Para 2 padres\n",
"NC=4 Es mejor Asíncrono con una diff de 0.13\n",
"NC=8 Es mejor Asíncrono con una diff de 0.064\n",
"NC=16 Es mejor Asíncrono con una diff de 0.034\n",
"NC=32 Es mejor Síncrono con una diff de 0.151\n",
"Para 4 padres\n",
"NC=2 Es mejor Asíncrono con una diff de 0.637\n",
"NC=8 Es mejor Asíncrono con una diff de 0.142\n",
"NC=16 Es mejor Síncrono con una diff de 0.28\n",
"NC=32 Es mejor Síncrono con una diff de 0.499\n",
"Para 8 padres\n",
"NC=2 Es mejor Asíncrono con una diff de 1.575\n",
"NC=4 Es mejor Asíncrono con una diff de 0.344\n",
"NC=16 Es mejor Síncrono con una diff de 0.32\n",
"NC=32 Es mejor Síncrono con una diff de 0.278\n",
"Para 16 padres\n",
"NC=2 Es mejor Asíncrono con una diff de 6.404\n",
"NC=4 Es mejor Asíncrono con una diff de 1.803\n",
"NC=8 Es mejor Asíncrono con una diff de 0.308\n",
"NC=32 Es mejor Síncrono con una diff de 0.113\n",
"Para 32 padres\n",
"NC=2 Es mejor Asíncrono con una diff de 18.438\n",
"NC=4 Es mejor Asíncrono con una diff de 4.533\n",
"NC=8 Es mejor Asíncrono con una diff de 1.927\n",
"NC=16 Es mejor Asíncrono con una diff de 0.481\n"
]
}
],
"source": [
"iters = dfM['Iters'].mean()\n",
"resultados = [0,0]\n",
"for dist in [1,2]:\n",
" print(\"Distribución \" + dist_names[dist] + \" -------------------------\")\n",
" dist_v = str(dist)+\",\"+str(dist)\n",
" for numP in values:\n",
" print(\"Para \", numP, \" padres\")\n",
" for numC in values:\n",
" if numP != numC:\n",
" Titer = dfL[(dfL[\"Tt\"] == 0)][(dfL[\"Dist\"] == dist)][(dfL.NP == numC)]['Ti'].mean() #Tiempo por iteracion\n",
" i=0\n",
" for adr in [0.0, 100.0]:\n",
" \n",
" auxExp = dfM[(dfM[\"Dist\"] == dist_v)][(dfM[\"%Async\"] == adr)][(dfM.NP == numP)][(dfM.NS == numC)]\n",
" Tr = auxExp['TS'].mean() + auxExp['TA'].mean() #Tiempo de redistribucion\n",
" M_it = dfL[(dfL[\"Tt\"] == 1)][(dfL[\"Dist\"] == dist)][(dfL[\"%Async\"] == adr)][(dfL.NP == numP)][(dfL.NS == numC)]['Ti'].count()/3 #Iteraciones asincronas\n",
" #No se presupone una diferencia temporal entre iteraciones sincronas y asincronas\n",
" if(M_it > iters):\n",
" M_it = iters\n",
" resultados[i] = (iters - M_it) * Titer + Tr\n",
" i+=1\n",
" #print(M_it)\n",
" #print(Titer)\n",
" #print((iters - M_it) * Titer)\n",
" #print(Tr)\n",
" #print(\"End\")\n",
" \n",
" if resultados[0] > resultados[1]:\n",
" mejor = \"Asíncrono\"\n",
" else:\n",
" mejor = \"Síncrono\"\n",
" diff = abs(round(resultados[0] - resultados[1], 3))\n",
" print(\"NC=\"+ str(numC) + \" Es mejor \" + mejor + \" con una diff de \"+ str(diff))\n",
" #TODO Comprobar"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
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" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>Ti</th>\n",
" <th>Iters</th>\n",
" <th>To</th>\n",
" <th>Iters2</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Tt</th>\n",
" <th>Dist</th>\n",
" <th>%Async</th>\n",
" <th>NP</th>\n",
" <th>NS</th>\n",
" <th></th>\n",
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" <th></th>\n",
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" <td rowspan=\"5\" valign=\"top\">0.0</td>\n",
" <td rowspan=\"4\" valign=\"top\">2</td>\n",
" <td>4</td>\n",
" <td>0.099861</td>\n",
" <td>500.0</td>\n",
" <td>112.000000</td>\n",
" <td>500.0</td>\n",
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" <tr>\n",
" <td>8</td>\n",
" <td>0.099849</td>\n",
" <td>500.0</td>\n",
" <td>112.000000</td>\n",
" <td>500.0</td>\n",
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" <tr>\n",
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" <td>500.0</td>\n",
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" <tr>\n",
" <td>32</td>\n",
" <td>0.099853</td>\n",
" <td>500.0</td>\n",
" <td>112.000000</td>\n",
" <td>500.0</td>\n",
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" <tr>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>0.049642</td>\n",
" <td>500.0</td>\n",
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" <td rowspan=\"5\" valign=\"top\">2</td>\n",
" <td rowspan=\"5\" valign=\"top\">100.0</td>\n",
" <td>16</td>\n",
" <td>32</td>\n",
" <td>0.034899</td>\n",
" <td>21.0</td>\n",
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" <td>7.000000</td>\n",
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" <tr>\n",
" <td>4</td>\n",
" <td>0.015599</td>\n",
" <td>54.0</td>\n",
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" <td>0.012685</td>\n",
" <td>54.0</td>\n",
" <td>7.000000</td>\n",
" <td>54.0</td>\n",
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" <tr>\n",
" <td>16</td>\n",
" <td>0.015421</td>\n",
" <td>44.0</td>\n",
" <td>7.000000</td>\n",
" <td>44.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>360 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" Ti Iters To Iters2\n",
"Tt Dist %Async NP NS \n",
"0.0 1 0.0 2 4 0.099861 500.0 112.000000 500.0\n",
" 8 0.099849 500.0 112.000000 500.0\n",
" 16 0.099860 500.0 112.000000 500.0\n",
" 32 0.099853 500.0 112.000000 500.0\n",
" 4 2 0.049642 500.0 55.666667 500.0\n",
"... ... ... ... ...\n",
"1.0 2 100.0 16 32 0.034899 21.0 14.000000 21.0\n",
" 32 2 0.012084 101.0 7.000000 101.0\n",
" 4 0.015599 54.0 7.000000 54.0\n",
" 8 0.012685 54.0 7.000000 54.0\n",
" 16 0.015421 44.0 7.000000 44.0\n",
"\n",
"[360 rows x 4 columns]"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grouped_aggL"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for dist in [1,2]:\n",
" v1 = grouped_aggL.loc[(0,dist,100.0,slice(None))]\n",
" v2 = grouped_aggL.loc[(1,dist,100.0,slice(None))]\n",
" aux_aggL = v1['Ti'].values / v2['Ti'].values\n",
"\n",
" colors = ['r', 'orange', 'g', 'm', 'y']\n",
" markers = ['+', 'x', '1', '2', 'X']\n",
"\n",
" f=plt.figure(figsize=(10, 7))\n",
" ax1 = f.add_subplot(111)\n",
" plt.xlim(0, max(values)+1)\n",
" plt.ylim(0, 1.2)\n",
" plt.xticks(values)\n",
" ax1.set_ylabel('Decremento velocidad')\n",
" ax1.set_xlabel('Procesos hijo')\n",
" ax1.set_title(\"Aumento de velocidad en las iteraciones al realizar redistribuciones asíncronas\")\n",
"\n",
"\n",
" for i in range(len(values)):\n",
" numP = values[i]\n",
" c = colors[i]\n",
" \n",
" mini = i * (len(values)-1)\n",
" maxi = (i+1) * (len(values)-1)\n",
" array_values = aux_aggL[mini:maxi]\n",
" indexes = np.arange(len(values)-1)\n",
" aux_j=0\n",
" for j in range(len(values)):\n",
" if(values[j] != numP):\n",
" indexes[aux_j] = values[j]\n",
" aux_j+=1\n",
" \n",
" x = indexes\n",
" y = array_values\n",
" label = str(numP) + ' padres'\n",
" ax1.axvline(numP)\n",
" plt.plot(x, y, color=colors[i], label=label, marker=markers[1], markersize=10)\n",
" \n",
" ax1.axhline(1, color='k')\n",
" f.legend()\n",
" #f.tight_layout()\n",
" f.savefig(\"Images/\"+\"Iters\"+ dist_names[dist] +\"_SpeedUp\", format=\"png\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
%% Cell type:code id: tags:
``` python
%matplotlib inline
import pandas as pd
from pandas import DataFrame, Series
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats
import sys
```
%% Cell type:code id: tags:
``` python
matrixMalEX="data_GG.csv"
matrixMal="data_GM.csv"
matrixIt="data_L.csv"
n_qty=2 #CAMBIAR SEGUN LA CANTIDAD DE NODOS USADOS
repet = 3 * 2 #CAMBIAR EL PRIMER NUMERO SEGUN NUMERO DE EJECUCIONES POR CONFIG
p_value = 0.05
values = [2, 4, 8, 16, 32]
dist_names = ['null', 'BestFit', 'WorstFit']
```
%% Cell type:code id: tags:
``` python
def speedUp(arr, seq, df):
numP = df.loc[arr.index[0]].NP
return seq[( seq.NP == numP )]['EX'] / arr.mean()
```
%% Cell type:code id: tags:
``` python
dfG = pd.read_csv( matrixMalEX )
dfG = dfG.drop(columns=dfG.columns[0])
dfG['S'] = dfG['N']
dfG['N'] = dfG['S'] + dfG['%Async']
dfG['%Async'] = (dfG['%Async'] / dfG['N']) * 100
if(n_qty == 1):
group = dfG.groupby(['%Async', 'Groups'])['TE']
else:
group = dfG.groupby(['Dist', '%Async', 'Groups'])['TE']
#group
grouped_aggG = group.agg(['mean'])
grouped_aggG.rename(columns={'mean':'TE',}, inplace=True)
```
%% Cell type:code id: tags:
``` python
from natsort import index_natsorted
grouped_aggG.sort_values(
by="Groups",
key=lambda x: np.argsort(index_natsorted(df["Groups"]))
)
grouped_aggG
```
%% Cell type:code id: tags:
``` python
dfM = pd.read_csv( matrixMal )
dfM = dfM.drop(columns=dfM.columns[0])
dfM['S'] = dfM['N']
dfM['N'] = dfM['S'] + dfM['%Async']
dfM["TR"] = dfM["TC"] + dfM["TS"] + dfM["TA"]
dfM['%Async'] = (dfM['%Async'] / dfM['N']) * 100
if(n_qty == 1):
groupM = dfM.groupby(['%Async','NP', 'NS'])['TC', 'TS', 'TA', 'TR']
else:
groupM = dfM.groupby(['Dist', '%Async','NP', 'NS'])['TC', 'TS', 'TA', 'TR']
#group
grouped_aggM = groupM.agg(['mean'])
grouped_aggM.columns = grouped_aggM.columns.get_level_values(0)
```
%% Cell type:code id: tags:
``` python
dfL = pd.read_csv( matrixIt )
dfL = dfL.drop(columns=dfL.columns[0])
dfL['S'] = dfL['N']
dfL['N'] = dfL['S'] + dfL['%Async']
dfL['%Async'] = (dfL['%Async'] / dfL['N']) * 100
if(n_qty == 1):
groupL = dfL[dfL['NS'] != 0].groupby(['Tt', '%Async', 'NP', 'NS'])['Ti', 'To']
else:
groupL = dfL[dfL['NS'] != 0].groupby(['Tt', 'Dist', '%Async', 'NP', 'NS'])['Ti', 'To']
#group
grouped_aggL = groupL.agg(['mean', 'count'])
grouped_aggL.columns = grouped_aggL.columns.get_level_values(0)
grouped_aggL.set_axis(['Ti', 'Iters', 'To', 'Iters2'], axis='columns')
grouped_aggL['Iters'] = np.ceil(grouped_aggL['Iters']/6) # TODO Cambiar a repeticiones realizadas
grouped_aggL['Iters2'] = np.ceil(grouped_aggL['Iters2']/6)
```
%%%% Output: stream
/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:16: FutureWarning: set_axis currently defaults to operating inplace.
This will change in a future version of pandas, use inplace=True to avoid this warning.
app.launch_new_instance()
%% Cell type:code id: tags:
``` python
grouped_aggL.to_excel("resultL.xlsx")
grouped_aggM.to_excel("resultM.xlsx")
grouped_aggG.to_excel("resultG.xlsx")
```
%% Cell type:code id: tags:
``` python
dfG
```
%%%% Output: execute_result
N %Async Groups Dist Matrix Time Iters TE \
0 1000000000 0.0 8,32 2,2 100000 0.1 1000 31.525710
1 1000000000 0.0 8,32 2,2 100000 0.1 1000 33.315857
2 1000000000 0.0 8,32 2,2 100000 0.1 1000 33.347537
3 1000000000 75.0 4,32 1,1 100000 0.1 1000 57.219027
4 1000000000 75.0 4,32 1,1 100000 0.1 1000 55.469166
.. ... ... ... ... ... ... ... ...
595 1000000000 50.0 4,8 2,2 100000 0.1 1000 73.771357
596 1000000000 50.0 4,8 2,2 100000 0.1 1000 75.557508
597 1000000000 50.0 16,8 1,1 100000 0.1 1000 35.949913
598 1000000000 50.0 16,8 1,1 100000 0.1 1000 37.900348
599 1000000000 50.0 16,8 1,1 100000 0.1 1000 36.012772
S
0 1000000000
1 1000000000
2 1000000000
3 250000000
4 250000000
.. ...
595 500000000
596 500000000
597 500000000
598 500000000
599 500000000
[600 rows x 9 columns]
%% Cell type:code id: tags:
``` python
grouped_aggG
```
%%%% Output: execute_result
TE
Dist %Async Groups
1,1 0.0 16,2 112.087769
16,32 18.394706
16,4 61.887052
16,8 37.986463
2,16 113.187865
... ...
2,2 100.0 4,8 74.864688
8,16 37.999586
8,2 123.584176
8,32 31.730297
8,4 74.052463
[200 rows x 1 columns]
%% Cell type:code id: tags:
``` python
dfM
```
%%%% Output: execute_result
N %Async NP NS Dist Matrix Time Iters TC TS \
0 1000000000 0.0 8 32 2,2 100000 0.1 1000 1.341434 0.695848
1 1000000000 0.0 8 32 2,2 100000 0.1 1000 1.405911 0.639849
2 1000000000 0.0 8 32 2,2 100000 0.1 1000 1.346017 0.747863
3 1000000000 75.0 4 32 1,1 100000 0.1 1000 0.688062 0.064900
4 1000000000 75.0 4 32 1,1 100000 0.1 1000 0.702200 0.068039
.. ... ... .. .. ... ... ... ... ... ...
595 1000000000 50.0 4 8 2,2 100000 0.1 1000 0.390444 0.142287
596 1000000000 50.0 4 8 2,2 100000 0.1 1000 0.351690 0.154699
597 1000000000 50.0 16 8 1,1 100000 0.1 1000 0.337472 0.081077
598 1000000000 50.0 16 8 1,1 100000 0.1 1000 0.351262 0.103176
599 1000000000 50.0 16 8 1,1 100000 0.1 1000 0.349151 0.094121
TA S TR
0 0.000000 1000000000 2.037282
1 0.000000 1000000000 2.045760
2 0.000000 1000000000 2.093880
3 0.364941 250000000 1.117903
4 0.412072 250000000 1.182311
.. ... ... ...
595 0.338576 500000000 0.871307
596 0.354917 500000000 0.861306
597 0.243481 500000000 0.662030
598 0.261565 500000000 0.716003
599 0.221750 500000000 0.665022
[600 rows x 13 columns]
%% Cell type:code id: tags:
``` python
grouped_aggM
```
%%%% Output: execute_result
TC TS TA TR
Dist %Async NP NS
1,1 0.0 2 4 0.220380 0.314893 0.000000 0.535273
8 0.248143 0.375003 0.000000 0.623146
16 0.339042 0.486650 0.000000 0.825692
32 0.743840 0.506385 0.000000 1.250225
4 2 0.197858 0.311136 0.000000 0.508994
... ... ... ... ...
2,2 100.0 16 32 1.358891 0.000000 1.419016 2.777907
32 2 0.591882 0.000000 2.477300 3.069183
4 0.804965 0.000000 1.729638 2.534603
8 0.935128 0.000000 1.520682 2.455810
16 1.221248 0.000000 1.541904 2.763152
[200 rows x 4 columns]
%% Cell type:code id: tags:
``` python
dfL
```
%%%% Output: execute_result
N %Async NP N_par NS Dist Matrix Time Iters \
0 1000000000 0.0 32 8 0 2 100000 0.1 1000
1 1000000000 0.0 32 8 0 2 100000 0.1 1000
2 1000000000 0.0 32 8 0 2 100000 0.1 1000
3 1000000000 0.0 32 8 0 2 100000 0.1 1000
4 1000000000 0.0 32 8 0 2 100000 0.1 1000
... ... ... .. ... .. ... ... ... ...
1199995 1000000000 50.0 8 16 0 1 100000 0.1 1000
1199996 1000000000 50.0 8 16 0 1 100000 0.1 1000
1199997 1000000000 50.0 8 16 0 1 100000 0.1 1000
1199998 1000000000 50.0 8 16 0 1 100000 0.1 1000
1199999 1000000000 50.0 8 16 0 1 100000 0.1 1000
Ti Tt To S
0 0.005463 0.0 6.0 1000000000
1 0.005350 0.0 6.0 1000000000
2 0.005355 0.0 6.0 1000000000
3 0.005354 0.0 6.0 1000000000
4 0.005352 0.0 6.0 1000000000
... ... ... ... ...
1199995 0.024075 0.0 27.0 500000000
1199996 0.024076 0.0 27.0 500000000
1199997 0.024076 0.0 27.0 500000000
1199998 0.024411 0.0 27.0 500000000
1199999 0.024075 0.0 27.0 500000000
[1200000 rows x 13 columns]
%% Cell type:code id: tags:
``` python
grouped_aggL
```
%%%% Output: execute_result
Ti Iters To Iters2
Tt Dist %Async NP NS
0.0 1 0.0 2 4 0.099861 500.0 112.000000 500.0
8 0.099849 500.0 112.000000 500.0
16 0.099860 500.0 112.000000 500.0
32 0.099853 500.0 112.000000 500.0
4 2 0.049642 500.0 55.666667 500.0
... ... ... ... ...
1.0 2 100.0 16 32 0.034899 21.0 14.000000 21.0
32 2 0.012084 101.0 7.000000 101.0
4 0.015599 54.0 7.000000 54.0
8 0.012685 54.0 7.000000 54.0
16 0.015421 44.0 7.000000 44.0
[360 rows x 4 columns]
%% Cell type:code id: tags:
``` python
print("TIEMPO EJECUCCION")
for dist in [1,2]:
print("Distribución " + dist_names[dist] + " -------------------------")
dist_v = str(dist)+","+str(dist)
for numP in values:
print("Para ", numP, " padres")
for numC in values:
if numP != numC:
group = str(numP) + "," + str(numC)
v1 = dfG[(dfG["%Async"] == 0.0)][(dfG.Groups == group)][(dfG["Dist"] == dist_v)]['TE']
v2 = dfG[(dfG["%Async"] == 100.0)][(dfG.Groups == group)][(dfG["Dist"] == dist_v)]['TE']
res = stats.ttest_ind(v1, v2)
diff = grouped_aggG['TE'].loc[(dist_v, 0.0, group)] - grouped_aggG['TE'].loc[(dist_v, 100.0, group)]
if diff > 0:
mejor = "Asíncrono"
else:
mejor = "Síncrono"
if res[1] < p_value:
print("EX numC=", numC, "p =", round(res[1],3), "Diff =", abs(round(diff,3)), mejor)
```
%%%% Output: stream
TIEMPO EJECUCCION
Distribución BestFit -------------------------
Para 2 padres
EX numC= 4 p = 0.039 Diff = 0.09 Asíncrono
Para 4 padres
Para 8 padres
EX numC= 4 p = 0.0 Diff = 1.411 Síncrono
Para 16 padres
EX numC= 2 p = 0.014 Diff = 3.662 Asíncrono
Para 32 padres
EX numC= 4 p = 0.002 Diff = 4.589 Asíncrono
Distribución WorstFit -------------------------
Para 2 padres
Para 4 padres
EX numC= 16 p = 0.046 Diff = 0.304 Síncrono
EX numC= 32 p = 0.012 Diff = 0.542 Síncrono
Para 8 padres
%%%% Output: stream
/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
# Remove the CWD from sys.path while we load stuff.
/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
# This is added back by InteractiveShellApp.init_path()
%%%% Output: stream
Para 16 padres
EX numC= 2 p = 0.023 Diff = 6.349 Asíncrono
EX numC= 4 p = 0.0 Diff = 1.799 Asíncrono
EX numC= 8 p = 0.046 Diff = 0.261 Asíncrono
Para 32 padres
EX numC= 2 p = 0.01 Diff = 18.514 Asíncrono
EX numC= 4 p = 0.0 Diff = 4.454 Asíncrono
EX numC= 8 p = 0.003 Diff = 1.92 Asíncrono
%% Cell type:code id: tags:
``` python
print("TIEMPO MALLEABILITY")
for dist in [1,2]:
print("Distribución " + dist_names[dist] + " -------------------------")
dist_v = str(dist)+","+str(dist)
for numP in values:
print("Para ", numP, " padres")
for numC in values:
if numP != numC:
v1 = dfM[(dfM["%Async"] == 0.0)][(dfM.NP == numP)][(dfM.NS == numC)][(dfM["Dist"] == dist_v)]['TS']
v2 = dfM[(dfM["%Async"] == 100.0)][(dfM.NP == numP)][(dfM.NS == numC)][(dfM["Dist"] == dist_v)]['TA']
res = stats.ttest_ind(v1, v2)
diff = grouped_aggM['TS'].loc[(dist_v, 0.0, numP, numC)] - grouped_aggM['TA'].loc[(dist_v, 100.0, numP, numC)]
if diff > 0:
mejor = "Asíncrono"
else:
mejor = "Síncrono"
if res[1] < p_value:
print("TR numC=", numC, "p =", round(res[1],3), "Diff =", abs(round(diff,3)), mejor)
```
%%%% Output: stream
TIEMPO MALLEABILITY
Distribución 1 -------------------------
Para 2 padres
TR numC= 8 p = 0.0 Diff = 0.029 Síncrono
Para 4 padres
TR numC= 8 p = 0.006 Diff = 0.043 Síncrono
TR numC= 32 p = 0.025 Diff = 0.052 Síncrono
Para 8 padres
TR numC= 4 p = 0.016 Diff = 0.022 Asíncrono
TR numC= 16 p = 0.009 Diff = 0.031 Síncrono
TR numC= 32 p = 0.0 Diff = 0.483 Síncrono
Para 16 padres
TR numC= 8 p = 0.002 Diff = 0.072 Síncrono
TR numC= 32 p = 0.019 Diff = 0.549 Síncrono
Para 32 padres
TR numC= 4 p = 0.002 Diff = 0.981 Síncrono
TR numC= 8 p = 0.001 Diff = 0.972 Síncrono
TR numC= 16 p = 0.001 Diff = 0.831 Síncrono
Distribución 2 -------------------------
Para 2 padres
TR numC= 4 p = 0.0 Diff = 0.069 Síncrono
%%%% Output: stream
/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:9: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
if __name__ == '__main__':
/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
# Remove the CWD from sys.path while we load stuff.
%%%% Output: stream
TR numC= 8 p = 0.029 Diff = 0.035 Síncrono
Para 4 padres
TR numC= 8 p = 0.02 Diff = 0.031 Síncrono
TR numC= 32 p = 0.009 Diff = 0.601 Síncrono
Para 8 padres
TR numC= 16 p = 0.011 Diff = 0.729 Síncrono
TR numC= 32 p = 0.018 Diff = 0.423 Síncrono
Para 16 padres
TR numC= 4 p = 0.001 Diff = 0.884 Síncrono
TR numC= 32 p = 0.021 Diff = 0.36 Síncrono
Para 32 padres
TR numC= 2 p = 0.029 Diff = 1.548 Síncrono
TR numC= 4 p = 0.008 Diff = 0.84 Síncrono
TR numC= 8 p = 0.011 Diff = 0.726 Síncrono
TR numC= 16 p = 0.004 Diff = 0.606 Síncrono
%% Cell type:code id: tags:
``` python
print("TIEMPO Iters")
for dist in [1,2]:
print("Distribución " + dist_names[dist] + " -------------------------")
dist_v = str(dist)+","+str(dist)
for numP in values:
print("Para ", numP, " padres")
for numC in values:
if numP != numC:
#exp = dfL[(dfL["Tt"] == 0)][(dfL["Dist"] == 1)][(dfL["%Async"] == 0.0)][(dfL.NP == numP)][(dfL.NS == numC)]
#TimeOp = exp['Ti']
#print(TimeOp)
v1 = dfL[(dfL["Tt"] == 0)][(dfL["Dist"] == dist)][(dfL["%Async"] == 100.0)][(dfL.NP == numP)][(dfL.NS == numC)]['Ti']
v2 = dfL[(dfL["Tt"] == 1)][(dfL["Dist"] == dist)][(dfL["%Async"] == 100.0)][(dfL.NP == numP)][(dfL.NS == numC)]['Ti']
res = stats.ttest_ind(v1, v2, equal_var = False)
diff = grouped_aggL['Ti'].loc[(0, dist, 0.0, numP, numC)] - grouped_aggL['Ti'].loc[(1, dist, 100.0, numP, numC)]
if diff > 0:
mejor = "Asíncrono"
else:
mejor = "Síncrono"
if res[1] < p_value:
#and abs(diff) > grouped_aggL['Ti'].loc[(0, dist, 0.0, numP, numC)]
print("Ti numC=", numC, "p =", round(res[1],3), "Diff =", abs(round(diff,4)), mejor)
```
%%%% Output: stream
TIEMPO Iters
Distribución BestFit -------------------------
Para 2 padres
Ti numC= 4 p = 0.035 Diff = 0.0001 Síncrono
%%%% Output: stream
/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
if sys.path[0] == '':
/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
del sys.path[0]
%%%% Output: stream
Ti numC= 8 p = 0.025 Diff = 0.0001 Síncrono
Ti numC= 16 p = 0.002 Diff = 0.0001 Síncrono
Ti numC= 32 p = 0.007 Diff = 0.0001 Síncrono
Para 4 padres
Ti numC= 16 p = 0.0 Diff = 0.0006 Síncrono
Para 8 padres
Ti numC= 4 p = 0.0 Diff = 0.0009 Síncrono
Ti numC= 32 p = 0.007 Diff = 0.0029 Síncrono
Para 16 padres
Ti numC= 32 p = 0.0 Diff = 0.0097 Síncrono
Para 32 padres
Ti numC= 2 p = 0.0 Diff = 0.0029 Síncrono
Ti numC= 4 p = 0.0 Diff = 0.0056 Síncrono
Ti numC= 8 p = 0.0 Diff = 0.0055 Síncrono
Ti numC= 16 p = 0.0 Diff = 0.0053 Síncrono
Distribución WorstFit -------------------------
Para 2 padres
Ti numC= 32 p = 0.024 Diff = 0.0256 Síncrono
Para 4 padres
Ti numC= 16 p = 0.0 Diff = 0.0243 Síncrono
Ti numC= 32 p = 0.0 Diff = 0.0343 Síncrono
Para 8 padres
Ti numC= 4 p = 0.036 Diff = 0.0044 Síncrono
Ti numC= 16 p = 0.0 Diff = 0.008 Síncrono
Ti numC= 32 p = 0.0 Diff = 0.0225 Síncrono
Para 16 padres
Ti numC= 2 p = 0.0 Diff = 0.0067 Síncrono
Ti numC= 4 p = 0.0 Diff = 0.0092 Síncrono
Ti numC= 8 p = 0.0 Diff = 0.0098 Síncrono
Ti numC= 32 p = 0.0 Diff = 0.0224 Síncrono
Para 32 padres
Ti numC= 2 p = 0.0 Diff = 0.0058 Síncrono
Ti numC= 4 p = 0.0 Diff = 0.0094 Síncrono
Ti numC= 8 p = 0.0 Diff = 0.0064 Síncrono
Ti numC= 16 p = 0.0 Diff = 0.0092 Síncrono
%% Cell type:code id: tags:
``` python
iters = dfM['Iters'].mean()
resultados = [0,0]
for dist in [1,2]:
print("Distribución " + dist_names[dist] + " -------------------------")
dist_v = str(dist)+","+str(dist)
for numP in values:
print("Para ", numP, " padres")
for numC in values:
if numP != numC:
Titer = dfL[(dfL["Tt"] == 0)][(dfL["Dist"] == dist)][(dfL.NP == numC)]['Ti'].mean() #Tiempo por iteracion
i=0
for adr in [0.0, 100.0]:
auxExp = dfM[(dfM["Dist"] == dist_v)][(dfM["%Async"] == adr)][(dfM.NP == numP)][(dfM.NS == numC)]
Tr = auxExp['TS'].mean() + auxExp['TA'].mean() #Tiempo de redistribucion
M_it = dfL[(dfL["Tt"] == 1)][(dfL["Dist"] == dist)][(dfL["%Async"] == adr)][(dfL.NP == numP)][(dfL.NS == numC)]['Ti'].count()/3 #Iteraciones asincronas
#No se presupone una diferencia temporal entre iteraciones sincronas y asincronas
if(M_it > iters):
M_it = iters
resultados[i] = (iters - M_it) * Titer + Tr
i+=1
#print(M_it)
#print(Titer)
#print((iters - M_it) * Titer)
#print(Tr)
#print("End")
if resultados[0] > resultados[1]:
mejor = "Asíncrono"
else:
mejor = "Síncrono"
diff = abs(round(resultados[0] - resultados[1], 3))
print("NC="+ str(numC) + " Es mejor " + mejor + " con una diff de "+ str(diff))
#TODO Comprobar
```
%%%% Output: stream
Distribución BestFit -------------------------
Para 2 padres
NC=4 Es mejor Asíncrono con una diff de 0.123
%%%% Output: stream
/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
# Remove the CWD from sys.path while we load stuff.
/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:16: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
app.launch_new_instance()
%%%% Output: stream
NC=8 Es mejor Asíncrono con una diff de 0.07
NC=16 Es mejor Asíncrono con una diff de 0.046
NC=32 Es mejor Asíncrono con una diff de 0.014
Para 4 padres
NC=2 Es mejor Asíncrono con una diff de 0.645
NC=8 Es mejor Asíncrono con una diff de 0.13
NC=16 Es mejor Asíncrono con una diff de 0.052
NC=32 Es mejor Síncrono con una diff de 0.005
Para 8 padres
NC=2 Es mejor Asíncrono con una diff de 1.523
NC=4 Es mejor Asíncrono con una diff de 0.354
NC=16 Es mejor Asíncrono con una diff de 0.066
NC=32 Es mejor Síncrono con una diff de 0.327
Para 16 padres
NC=2 Es mejor Asíncrono con una diff de 3.676
NC=4 Es mejor Asíncrono con una diff de 1.235
NC=8 Es mejor Asíncrono con una diff de 0.406
NC=32 Es mejor Síncrono con una diff de 0.304
Para 32 padres
NC=2 Es mejor Asíncrono con una diff de 16.171
NC=4 Es mejor Asíncrono con una diff de 4.551
NC=8 Es mejor Asíncrono con una diff de 1.599
NC=16 Es mejor Asíncrono con una diff de 0.12
Distribución WorstFit -------------------------
Para 2 padres
NC=4 Es mejor Asíncrono con una diff de 0.13
NC=8 Es mejor Asíncrono con una diff de 0.064
NC=16 Es mejor Asíncrono con una diff de 0.034
NC=32 Es mejor Síncrono con una diff de 0.151
Para 4 padres
NC=2 Es mejor Asíncrono con una diff de 0.637
NC=8 Es mejor Asíncrono con una diff de 0.142
NC=16 Es mejor Síncrono con una diff de 0.28
NC=32 Es mejor Síncrono con una diff de 0.499
Para 8 padres
NC=2 Es mejor Asíncrono con una diff de 1.575
NC=4 Es mejor Asíncrono con una diff de 0.344
NC=16 Es mejor Síncrono con una diff de 0.32
NC=32 Es mejor Síncrono con una diff de 0.278
Para 16 padres
NC=2 Es mejor Asíncrono con una diff de 6.404
NC=4 Es mejor Asíncrono con una diff de 1.803
NC=8 Es mejor Asíncrono con una diff de 0.308
NC=32 Es mejor Síncrono con una diff de 0.113
Para 32 padres
NC=2 Es mejor Asíncrono con una diff de 18.438
NC=4 Es mejor Asíncrono con una diff de 4.533
NC=8 Es mejor Asíncrono con una diff de 1.927
NC=16 Es mejor Asíncrono con una diff de 0.481
%% Cell type:code id: tags:
``` python
grouped_aggL
```
%%%% Output: execute_result
Ti Iters To Iters2
Tt Dist %Async NP NS
0.0 1 0.0 2 4 0.099861 500.0 112.000000 500.0
8 0.099849 500.0 112.000000 500.0
16 0.099860 500.0 112.000000 500.0
32 0.099853 500.0 112.000000 500.0
4 2 0.049642 500.0 55.666667 500.0
... ... ... ... ...
1.0 2 100.0 16 32 0.034899 21.0 14.000000 21.0
32 2 0.012084 101.0 7.000000 101.0
4 0.015599 54.0 7.000000 54.0
8 0.012685 54.0 7.000000 54.0
16 0.015421 44.0 7.000000 44.0
[360 rows x 4 columns]
%% Cell type:code id: tags:
``` python
for dist in [1,2]:
v1 = grouped_aggL.loc[(0,dist,100.0,slice(None))]
v2 = grouped_aggL.loc[(1,dist,100.0,slice(None))]
aux_aggL = v1['Ti'].values / v2['Ti'].values
colors = ['r', 'orange', 'g', 'm', 'y']
markers = ['+', 'x', '1', '2', 'X']
f=plt.figure(figsize=(10, 7))
ax1 = f.add_subplot(111)
plt.xlim(0, max(values)+1)
plt.ylim(0, 1.2)
plt.xticks(values)
ax1.set_ylabel('Decremento velocidad')
ax1.set_xlabel('Procesos hijo')
ax1.set_title("Aumento de velocidad en las iteraciones al realizar redistribuciones asíncronas")
for i in range(len(values)):
numP = values[i]
c = colors[i]
mini = i * (len(values)-1)
maxi = (i+1) * (len(values)-1)
array_values = aux_aggL[mini:maxi]
indexes = np.arange(len(values)-1)
aux_j=0
for j in range(len(values)):
if(values[j] != numP):
indexes[aux_j] = values[j]
aux_j+=1
x = indexes
y = array_values
label = str(numP) + ' padres'
ax1.axvline(numP)
plt.plot(x, y, color=colors[i], label=label, marker=markers[1], markersize=10)
ax1.axhline(1, color='k')
f.legend()
#f.tight_layout()
f.savefig("Images/"+"Iters"+ dist_names[dist] +"_SpeedUp", format="png")
```
%%%% Output: display_data
[Hidden Image Output]
%%%% Output: display_data
[Hidden Image Output]
%% Cell type:code id: tags:
``` python
```
Analysis/.ipynb_checkpoints/MallTimes-checkpoint.py 0 → 100644
View file @ a3730724
import sys
import glob
import numpy as np
import pandas as pd
def getData(lineS, outData, tp, hasIter = False):
for data in lineS:
k_v = data.split('=')
if k_v[0] == "time":
time = float(k_v[1])
elif k_v[0] == "iters" and hasIter:
iters = int(k_v[1])
outData[tp] = time
if hasIter:
outData[tp+1] = iters
#-----------------------------------------------
def record(f, observation, line):
# Record first line - General info
lineS = line.split()
for j in range(1,7):
observation[j] = int(lineS[j].split('=')[1])
# Record procces number
line = next(f)
lineS = line.split()
j = 7
for key_values in lineS:
k_v = key_values.split('=')
observation[j] = int(k_v[1])
j+=1
# Record data
j = 9
for j in range(9, 13):
line = next(f)
lineS = line.split()
getData(lineS, observation, j)
line = next(f)
lineS = line.split()
#if observation[0] == "A":
getData(lineS, observation, 13, True)
#else:
# getData(lineS, observation, 13)
#-----------------------------------------------
def read_file(f, dataA, dataB, it):
recording = False
resizes = 0
timer = 0
previousNP = 0
for line in f:
lineS = line.split()
if len(lineS) > 0:
if lineS[0] == "Config": # CONFIG LINE
recording = True
it += 1
dataA.append([None]*8)
dataB.append([None]*11)
resizes = int(lineS[2].split('=')[1].split(',')[0])
matrix = int(lineS[3].split('=')[1].split(',')[0])
sdr = int(lineS[4].split('=')[1].split(',')[0])
adr = int(lineS[5].split('=')[1].split(',')[0]) #TODO Que lo tome como porcentaje
time = float(lineS[7].split('=')[1])
dataB[it][5] = matrix
dataB[it][0] = sdr
dataB[it][1] = adr
dataB[it][6] = time
dataB[it][4] = ""
dataA[it][4] = matrix
dataA[it][0] = sdr
dataA[it][1] = adr
dataA[it][5] = time
dataA[it][3] = ""
elif recording and resizes != 0: # RESIZE LINE
iters = int(lineS[2].split('=')[1].split(',')[0])
npr = int(lineS[3].split('=')[1].split(',')[0])
dist = lineS[5].split('=')[1]
dataB[it][7] = iters
dataA[it][6] = iters
resizes = resizes - 1
if resizes == 0:
dataB[it][3] = npr
dataB[it][4] += dist
dataA[it][3] += dist
dataA[it][2] = str(previousNP) + "," + str(npr)
timer = 3
else:
dataB[it][2] = npr
dataB[it][4] += dist + ","
dataA[it][3] += dist + ","
previousNP = npr
else: # SAVE TIMES
if timer == 3:
dataB[it][8] = float(lineS[1])
elif timer == 2:
dataB[it][9] = float(lineS[1])
elif timer == 1:
dataB[it][10] = float(lineS[1])
else:
dataA[it][7] = float(lineS[1])
timer = timer - 1
return it
#columnsA1 = ["N", "%Async", "Groups", "Dist", "Matrix", "Time", "Iters", "TE"] #7
#columnsB1 = ["N", "%Async", "NP", "NS", "Dist", "Matrix", "Time", "Iters", "TC", "TS", "TA"] #10
#Config loaded: resizes=2, matrix=1000, sdr=1000000000, adr=0, aib=0, time=2.000000 || grp=1
#Resize 0: Iters=100, Procs=2, Factors=1.000000, Phy=2
#Resize 1: Iters=100, Procs=4, Factors=0.500000, Phy=2
#Tspawn: 0.249393
#Tsync: 0.330391
#Tasync: 0
#Tex: 301.428615
#-----------------------------------------------
if len(sys.argv) < 2:
print("The files name is missing\nUsage: python3 iterTimes.py resultsName directory csvOutName")
exit(1)
if len(sys.argv) >= 3:
BaseDir = sys.argv[2]
print("Searching in directory: "+ BaseDir)
else:
BaseDir = sys.argv[2]
if len(sys.argv) >= 4:
print("Csv name will be: " + sys.argv[3] + ".csv")
name = sys.argv[3]
else:
name = "data"
insideDir = "Run"
lista = glob.glob("./" + BaseDir + insideDir + "*/" + sys.argv[1]+ "*Global.o*")
print("Number of files found: "+ str(len(lista)));
it = -1
dataA = []
dataB = []
columnsA = ["N", "%Async", "Groups", "Dist", "Matrix", "Time", "Iters", "TE"] #7
columnsB = ["N", "%Async", "NP", "NS", "Dist", "Matrix", "Time", "Iters", "TC", "TS", "TA"] #10
for elem in lista:
f = open(elem, "r")
it = read_file(f, dataA, dataB, it)
f.close()
#print(data)
dfA = pd.DataFrame(dataA, columns=columnsA)
dfA.to_csv(name + '_G.csv')
dfB = pd.DataFrame(dataB, columns=columnsB)
dfB.to_csv(name + '_M.csv')
Analysis/.ipynb_checkpoints/iterTimes-checkpoint.py 0 → 100644
View file @ a3730724
import sys
import glob
import numpy as np
import pandas as pd
#-----------------------------------------------
def read_file(f, data, it):
matrix = 0
sdr = 0
adr = 0
time = 0
recording = False
it_line = 0
aux_it = 0
iters = 0
np = 0
np_par = 0
ns = 0
for line in f:
lineS = line.split()
if len(lineS) > 1:
if recording:
aux_it = 0
if it_line==0:
lineS.pop(0)
for observation in lineS:
data.append([None]*11)
data[it+aux_it][0] = sdr
data[it+aux_it][1] = adr
data[it+aux_it][2] = np
data[it+aux_it][3] = np_par
data[it+aux_it][4] = ns
data[it+aux_it][5] = matrix
data[it+aux_it][6] = time
data[it+aux_it][7] = iters
data[it+aux_it][8] = float(observation)
aux_it+=1
it_line = it_line + 1
elif it_line==1:
lineS.pop(0)
for observation in lineS:
data[it+aux_it][9] = float(observation)
aux_it+=1
it_line = it_line + 1
else:
lineS.pop(0)
for observation in lineS:
data[it+aux_it][10] = float(observation)
aux_it+=1
it = it + aux_it
recording = False
it_line = 0
#TODO Que tome adr como porcentaje
if lineS[0] == "Config:":
matrix = int(lineS[1].split('=')[1].split(',')[0])
sdr = int(lineS[2].split('=')[1].split(',')[0])
adr = int(lineS[3].split('=')[1].split(',')[0])
time = float(lineS[5].split('=')[1])
elif lineS[0] == "Config":
recording = True
iters = int(lineS[2].split('=')[1].split(',')[0])
np = int(lineS[5].split('=')[1].split(',')[0])
np_par = int(lineS[6].split('=')[1].split(',')[0])
ns = int(float(lineS[7].split('=')[1]))
return it
#-----------------------------------------------
#Config: matrix=1000, sdr=1000000000, adr=0, aib=0 time=2.000000
#Config Group: iters=100, factor=1.000000, phy=2, procs=2, parents=0, sons=4
#Ttype: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
if len(sys.argv) < 2:
print("The files name is missing\nUsage: python3 iterTimes.py resultsName directory csvOutName")
exit(1)
if len(sys.argv) >= 3:
BaseDir = sys.argv[2]
print("Searching in directory: "+ BaseDir)
else:
BaseDir = sys.argv[2]
if len(sys.argv) >= 4:
print("Csv name will be: " + sys.argv[3] + ".csv")
name = sys.argv[3]
else:
name = "data"
insideDir = "Run"
lista = glob.glob("./" + BaseDir + insideDir + "*/" + sys.argv[1]+ "*ID*.o*")
print("Number of files found: "+ str(len(lista)));
it = 0
data = [] #0 #1 #2 #3 #4 #5 #6 #7 #8 #9 #10
columns = ["N", "%Async", "NP", "N_par", "NS", "Matrix", "Time", "Iters", "Ti", "Tt", "To"] #11
for elem in lista:
f = open(elem, "r")
it = read_file(f, data, it)
f.close()
#print(data)
df = pd.DataFrame(data, columns=columns)
df.to_csv(name + '.csv')
Analysis/MallTimes.py 0 → 100644
View file @ a3730724
import sys
import glob
import numpy as np
import pandas as pd
def getData(lineS, outData, tp, hasIter = False):
for data in lineS:
k_v = data.split('=')
if k_v[0] == "time":
time = float(k_v[1])
elif k_v[0] == "iters" and hasIter:
iters = int(k_v[1])
outData[tp] = time
if hasIter:
outData[tp+1] = iters
#-----------------------------------------------
def record(f, observation, line):
# Record first line - General info
lineS = line.split()
for j in range(1,7):
observation[j] = int(lineS[j].split('=')[1])
# Record procces number
line = next(f)
lineS = line.split()
j = 7
for key_values in lineS:
k_v = key_values.split('=')
observation[j] = int(k_v[1])
j+=1
# Record data
j = 9
for j in range(9, 13):
line = next(f)
lineS = line.split()
getData(lineS, observation, j)
line = next(f)
lineS = line.split()
#if observation[0] == "A":
getData(lineS, observation, 13, True)
#else:
# getData(lineS, observation, 13)
#-----------------------------------------------
def read_file(f, dataA, dataB, it):
recording = False
resizes = 0
timer = 0
previousNP = 0
for line in f:
lineS = line.split()
if len(lineS) > 0:
if lineS[0] == "Config": # CONFIG LINE
recording = True
it += 1
dataA.append([None]*9)
dataB.append([None]*12)
resizes = int(lineS[2].split('=')[1].split(',')[0])
compute_tam = int(lineS[3].split('=')[1].split(',')[0])
comm_tam = int(lineS[4].split('=')[1].split(',')[0])
sdr = int(lineS[5].split('=')[1].split(',')[0])
adr = int(lineS[6].split('=')[1].split(',')[0]) #TODO Que lo tome como porcentaje
# TODO Que obtenga Aib
time = float(lineS[8].split('=')[1])
dataB[it][0] = sdr
dataB[it][1] = adr
dataB[it][4] = ""
dataB[it][5] = compute_tam
dataB[it][6] = comm_tam
dataB[it][7] = time
dataB[it][8] = ""
dataA[it][0] = sdr
dataA[it][1] = adr
dataA[it][3] = ""
dataA[it][4] = compute_tam
dataA[it][5] = comm_tam
dataA[it][6] = time
dataA[it][7] = ""
elif recording and resizes != 0: # RESIZE LINE
iters = int(lineS[2].split('=')[1].split(',')[0])
npr = int(lineS[3].split('=')[1].split(',')[0])
dist = lineS[5].split('=')[1]
resizes = resizes - 1
if resizes == 0:
dataB[it][3] = npr
dataB[it][4] += dist
dataB[it][8] += str(iters)
dataA[it][2] = str(previousNP) + "," + str(npr)
dataA[it][3] += dist
dataA[it][7] += str(iters)
timer = 3
else:
dataB[it][2] = npr
dataB[it][4] += dist + ","
dataB[it][8] += str(iters) + ","
dataA[it][3] += dist + ","
dataA[it][7] += str(iters) + ","
previousNP = npr
else: # SAVE TIMES
if timer == 3:
dataB[it][9] = float(lineS[1])
elif timer == 2:
dataB[it][10] = float(lineS[1])
elif timer == 1:
dataB[it][11] = float(lineS[1])
else:
dataA[it][8] = float(lineS[1])
timer = timer - 1
return it
#columnsA1 = ["N", "%Async", "Groups", "Dist", "Matrix", "CommTam", "Time", "Iters", "TE"] #8
#columnsB1 = ["N", "%Async", "NP", "NS", "Dist", "Matrix", "CommTam", "Time", "Iters", "TC", "TS", "TA"] #11
#Config loaded: resizes=2, matrix=1000, sdr=1000000000, adr=0, aib=0, time=2.000000 || grp=1
#Resize 0: Iters=100, Procs=2, Factors=1.000000, Phy=2
#Resize 1: Iters=100, Procs=4, Factors=0.500000, Phy=2
#Tspawn: 0.249393
#Tsync: 0.330391
#Tasync: 0
#Tex: 301.428615
#-----------------------------------------------
if len(sys.argv) < 2:
print("The files name is missing\nUsage: python3 iterTimes.py resultsName directory csvOutName")
exit(1)
if len(sys.argv) >= 3:
BaseDir = sys.argv[2]
print("Searching in directory: "+ BaseDir)
else:
BaseDir = sys.argv[2]
if len(sys.argv) >= 4:
print("Csv name will be: " + sys.argv[3] + "G.csv & " + sys.argv[3] + "M.csv")
name = sys.argv[3]
else:
name = "data"
insideDir = "Run"
lista = glob.glob("./" + BaseDir + insideDir + "*/" + sys.argv[1]+ "*Global.o*")
print("Number of files found: "+ str(len(lista)));
it = -1
dataA = []
dataB = []
columnsA = ["N", "%Async", "Groups", "Dist", "Matrix", "CommTam", "Time", "Iters", "TE"] #8
columnsB = ["N", "%Async", "NP", "NS", "Dist", "Matrix", "CommTam", "Time", "Iters", "TC", "TS", "TA"] #11
for elem in lista:
f = open(elem, "r")
it = read_file(f, dataA, dataB, it)
f.close()
#print(data)
dfA = pd.DataFrame(dataA, columns=columnsA)
dfA.to_csv(name + 'G.csv')
dfB = pd.DataFrame(dataB, columns=columnsB)
dfB.to_csv(name + 'M.csv')
Analysis/README.md 0 → 100644
View file @ a3730724
Esta carpeta contiene códigos para poder analizar los resultados obtenidos.
Para utilizar los códigos es necesario Python con los módulos Numpy y Pandas.
El código analyser.ipynb necesita además de la aplicación JupyterLab.
Los códigos son los siguientes:
- Malltimes.py: Recoge los tiempos globales de maleabilidad y ejecución de todos los ficheros pasados como argumento y
los almacena en dos ficheros CSV para ser utilizados en analyser.ipynb
- Itertimes.py: Recoge los tiempos locales de iteraciones de un grupo de procesos de todos los ficheros pasados como
argumento y los almacena en un fichero CSV para ser utilizado en analyser.ipynb
+ Ejemplo de uso de ambos códigos (Esperan los mismos argumentos):
python3 Malltimes.py NombreFicheros DirectorioFicheros/ NombreCSV
NombreFicheros: La parte común de los ficheros, los códigos buscan solo aquellos nombres que empiecen por esta cadena.
Por defecto, con poner "R" es suficiente.
DirectorioFicheros/: Nombre del directorio donde se encuentran todos los resultados. Esta pensado para que busque
en todos las subdirectorios que tenga en el primer nivel, pero no en segundos niveles o más.
NombreCSV: Nombre del fichero CSV en el que escribir la recopilación de resultados.
- analyser.ipynb: Código para ser ejecutado por JupyterNotebook. Dentro del mismo hay que indicar los nombres de los
ficheros CSV a analizar y tras ellos ejecutar las celdas. Como resultado se obtienen tres ficheros XLSX e imagenes
en el directorio "Images", y además varios resultados sobre T-test entre varios resultados que se reflejan como
output en la salida estandar de JupyterNotebook.
Analysis/analyser.ipynb 0 → 100644
View file @ a3730724
{
"cells": [
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"from pandas import DataFrame, Series\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"from scipy import stats\n",
"import sys"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"matrixMalEX=\"data_GG.csv\"\n",
"matrixMal=\"data_GM.csv\"\n",
"matrixIt=\"data_L.csv\"\n",
"n_qty=2 #CAMBIAR SEGUN LA CANTIDAD DE NODOS USADOS\n",
"repet = 3 * 2 #CAMBIAR EL PRIMER NUMERO SEGUN NUMERO DE EJECUCIONES POR CONFIG\n",
"\n",
"p_value = 0.05\n",
"values = [2, 10, 20, 40]\n",
"dist_names = ['null', 'BestFit', 'WorstFit']"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"def speedUp(arr, seq, df):\n",
" numP = df.loc[arr.index[0]].NP\n",
" return seq[( seq.NP == numP )]['EX'] / arr.mean()"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"dfG = pd.read_csv( matrixMalEX )\n",
"\n",
"dfG = dfG.drop(columns=dfG.columns[0])\n",
"dfG['S'] = dfG['N']\n",
"dfG['N'] = dfG['S'] + dfG['%Async']\n",
"dfG['%Async'] = (dfG['%Async'] / dfG['N']) * 100\n",
"\n",
"if(n_qty == 1):\n",
" group = dfG.groupby(['%Async', 'Groups'])['TE']\n",
"else: \n",
" group = dfG.groupby(['Dist', '%Async', 'Groups'])['TE']\n",
"\n",
"#group\n",
"grouped_aggG = group.agg(['mean'])\n",
"grouped_aggG.rename(columns={'mean':'TE',}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"dfM = pd.read_csv( matrixMal )\n",
"dfM = dfM.drop(columns=dfM.columns[0])\n",
"\n",
"dfM['S'] = dfM['N']\n",
"dfM['N'] = dfM['S'] + dfM['%Async']\n",
"dfM[\"TR\"] = dfM[\"TC\"] + dfM[\"TS\"] + dfM[\"TA\"]\n",
"dfM['%Async'] = (dfM['%Async'] / dfM['N']) * 100\n",
"\n",
"if(n_qty == 1):\n",
" groupM = dfM.groupby(['%Async','NP', 'NS'])['TC', 'TS', 'TA', 'TR']\n",
"else:\n",
" groupM = dfM.groupby(['Dist', '%Async','NP', 'NS'])['TC', 'TS', 'TA', 'TR']\n",
"\n",
"#group\n",
"grouped_aggM = groupM.agg(['mean'])\n",
"grouped_aggM.columns = grouped_aggM.columns.get_level_values(0)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: FutureWarning: set_axis currently defaults to operating inplace.\n",
"This will change in a future version of pandas, use inplace=True to avoid this warning.\n",
" if sys.path[0] == '':\n"
]
}
],
"source": [
"dfL = pd.read_csv( matrixIt )\n",
"dfL = dfL.drop(columns=dfL.columns[0])\n",
"\n",
"if(n_qty == 1):\n",
" groupL = dfL[dfL['NS'] != 0].groupby(['Tt', '%Async', 'NP', 'NS'])['Ti', 'To']\n",
"else:\n",
" groupL = dfL[dfL['NS'] != 0].groupby(['Tt', 'Dist', '%Async', 'NP', 'NS'])['Ti', 'To']\n",
"\n",
"#group\n",
"grouped_aggL = groupL.agg(['mean', 'count'])\n",
"grouped_aggL.columns = grouped_aggL.columns.get_level_values(0)\n",
"grouped_aggL.set_axis(['Ti', 'Iters', 'To', 'Iters2'], axis='columns')\n",
"\n",
"grouped_aggL['Iters'] = np.ceil(grouped_aggL['Iters']/6) # TODO Cambiar a repeticiones realizadas\n",
"grouped_aggL['Iters2'] = np.ceil(grouped_aggL['Iters2']/6)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"grouped_aggL.to_excel(\"resultL.xlsx\") \n",
"grouped_aggM.to_excel(\"resultM.xlsx\") \n",
"grouped_aggG.to_excel(\"resultG.xlsx\") "
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>N</th>\n",
" <th>%Async</th>\n",
" <th>Groups</th>\n",
" <th>Dist</th>\n",
" <th>Matrix</th>\n",
" <th>CommTam</th>\n",
" <th>Time</th>\n",
" <th>Iters</th>\n",
" <th>TE</th>\n",
" <th>S</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>2000000000</td>\n",
" <td>0.0</td>\n",
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" <td>2,2</td>\n",
" <td>100000</td>\n",
" <td>0</td>\n",
" <td>0.2</td>\n",
" <td>1,10</td>\n",
" <td>3.325915</td>\n",
" <td>2000000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>2000000000</td>\n",
" <td>0.0</td>\n",
" <td>20,40</td>\n",
" <td>2,2</td>\n",
" <td>100000</td>\n",
" <td>0</td>\n",
" <td>0.2</td>\n",
" <td>1,10</td>\n",
" <td>3.661673</td>\n",
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" <tr>\n",
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" <td>2000000000</td>\n",
" <td>0.0</td>\n",
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" <td>100000</td>\n",
" <td>0</td>\n",
" <td>0.2</td>\n",
" <td>1,10</td>\n",
" <td>3.647241</td>\n",
" <td>2000000000</td>\n",
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" <tr>\n",
" <td>3</td>\n",
" <td>2000000000</td>\n",
" <td>75.0</td>\n",
" <td>10,40</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>475</td>\n",
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" <td>100000</td>\n",
" <td>0</td>\n",
" <td>0.2</td>\n",
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" <tr>\n",
" <td>476</td>\n",
" <td>2000000000</td>\n",
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" <td>0</td>\n",
" <td>0.2</td>\n",
" <td>1,10</td>\n",
" <td>1.683571</td>\n",
" <td>1000000000</td>\n",
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" <tr>\n",
" <td>477</td>\n",
" <td>2000000000</td>\n",
" <td>50.0</td>\n",
" <td>40,20</td>\n",
" <td>1,1</td>\n",
" <td>100000</td>\n",
" <td>0</td>\n",
" <td>0.2</td>\n",
" <td>1,10</td>\n",
" <td>3.107688</td>\n",
" <td>1000000000</td>\n",
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" <tr>\n",
" <td>478</td>\n",
" <td>2000000000</td>\n",
" <td>50.0</td>\n",
" <td>40,20</td>\n",
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" <td>100000</td>\n",
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" <td>0.2</td>\n",
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" <td>3.442101</td>\n",
" <td>1000000000</td>\n",
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"</table>\n",
"<p>480 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" N %Async Groups Dist Matrix CommTam Time Iters TE \\\n",
"0 2000000000 0.0 20,40 2,2 100000 0 0.2 1,10 3.325915 \n",
"1 2000000000 0.0 20,40 2,2 100000 0 0.2 1,10 3.661673 \n",
"2 2000000000 0.0 20,40 2,2 100000 0 0.2 1,10 3.647241 \n",
"3 2000000000 75.0 10,40 1,1 100000 0 0.2 1,10 2.710496 \n",
"4 2000000000 75.0 10,40 1,1 100000 0 0.2 1,10 2.785736 \n",
".. ... ... ... ... ... ... ... ... ... \n",
"475 2000000000 50.0 10,10 2,2 100000 0 0.2 1,10 1.292079 \n",
"476 2000000000 50.0 10,10 2,2 100000 0 0.2 1,10 1.683571 \n",
"477 2000000000 50.0 40,20 1,1 100000 0 0.2 1,10 3.107688 \n",
"478 2000000000 50.0 40,20 1,1 100000 0 0.2 1,10 3.169551 \n",
"479 2000000000 50.0 40,20 1,1 100000 0 0.2 1,10 3.442101 \n",
"\n",
" S \n",
"0 2000000000 \n",
"1 2000000000 \n",
"2 2000000000 \n",
"3 500000000 \n",
"4 500000000 \n",
".. ... \n",
"475 1000000000 \n",
"476 1000000000 \n",
"477 1000000000 \n",
"478 1000000000 \n",
"479 1000000000 \n",
"\n",
"[480 rows x 10 columns]"
]
},
"execution_count": 65,
"metadata": {},
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}
],
"source": [
"dfG"
]
},
{
"cell_type": "code",
"execution_count": 66,
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{
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" TE\n",
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" 10,2 7.495134\n",
" 10,20 1.052943\n",
" 10,40 2.412291\n",
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"... ...\n",
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" 40,10 3.869905\n",
" 40,2 4.056287\n",
" 40,20 4.384910\n",
" 40,40 3.143114\n",
"\n",
"[160 rows x 1 columns]"
]
},
"execution_count": 66,
"metadata": {},
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}
],
"source": [
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],
"text/plain": [
" N %Async NP NS Dist Matrix CommTam Time Iters TC \\\n",
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"479 2000000000 50.0 40 20 1,1 100000 0 0.2 1,10 1.101387 \n",
"\n",
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".. ... ... ... ... \n",
"475 0.091322 0.679278 1000000000 1.343735 \n",
"476 0.096622 1.075340 1000000000 1.740457 \n",
"477 0.557700 1.843437 1000000000 3.572510 \n",
"478 0.634888 1.881406 1000000000 3.744038 \n",
"479 0.695218 2.296932 1000000000 4.093537 \n",
"\n",
"[480 rows x 14 columns]"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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{
"cell_type": "code",
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{
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" <td>40</td>\n",
" <td>1.445946</td>\n",
" <td>0.000000</td>\n",
" <td>1.597486</td>\n",
" <td>3.043431</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>160 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" TC TS TA TR\n",
"Dist %Async NP NS \n",
"1,1 0.0 2 2 0.246172 0.658124 0.000000 0.904296\n",
" 10 0.372526 0.846183 0.000000 1.218709\n",
" 20 0.463237 0.975418 0.000000 1.438655\n",
" 40 5.897614 1.306502 0.000000 7.204116\n",
" 10 2 4.623390 0.823443 0.000000 5.446832\n",
"... ... ... ... ...\n",
"2,2 100.0 20 40 1.464316 0.000000 2.380671 3.844987\n",
" 40 2 0.482502 0.000000 3.510194 3.992695\n",
" 10 1.026574 0.000000 2.781301 3.807875\n",
" 20 1.397894 0.000000 2.814464 4.212358\n",
" 40 1.445946 0.000000 1.597486 3.043431\n",
"\n",
"[160 rows x 4 columns]"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grouped_aggM"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>N</th>\n",
" <th>%Async</th>\n",
" <th>NP</th>\n",
" <th>N_par</th>\n",
" <th>NS</th>\n",
" <th>Dist</th>\n",
" <th>Compute_tam</th>\n",
" <th>Comm_tam</th>\n",
" <th>Time</th>\n",
" <th>Iters</th>\n",
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],
"text/plain": [
" N %Async NP N_par NS Dist Compute_tam Comm_tam Time \\\n",
"0 2000000000 0.0 40 20 0 2 100000 0 0.2 \n",
"1 2000000000 0.0 40 20 0 2 100000 0 0.2 \n",
"2 2000000000 0.0 40 20 0 2 100000 0 0.2 \n",
"3 2000000000 0.0 40 20 0 2 100000 0 0.2 \n",
"4 2000000000 0.0 40 20 0 2 100000 0 0.2 \n",
"... ... ... .. ... .. ... ... ... ... \n",
"14951 2000000000 50.0 40 0 20 1 100000 0 0.2 \n",
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"14953 2000000000 50.0 40 0 20 1 100000 0 0.2 \n",
"14954 2000000000 50.0 40 0 20 1 100000 0 0.2 \n",
"14955 2000000000 50.0 40 0 20 1 100000 0 0.2 \n",
"\n",
" Iters Ti Tt To \n",
"0 10 0.009826 0.0 11.0 \n",
"1 10 0.025812 0.0 11.0 \n",
"2 10 0.009819 0.0 11.0 \n",
"3 10 0.011071 0.0 11.0 \n",
"4 10 0.009815 0.0 11.0 \n",
"... ... ... ... ... \n",
"14951 1 0.025818 1.0 11.0 \n",
"14952 1 0.009812 1.0 11.0 \n",
"14953 1 0.025813 1.0 11.0 \n",
"14954 1 0.025810 1.0 11.0 \n",
"14955 1 0.009820 1.0 11.0 \n",
"\n",
"[14956 rows x 13 columns]"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfL"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
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" <th>Iters2</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Tt</th>\n",
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"</div>"
],
"text/plain": [
" Ti Iters To Iters2\n",
"Tt Dist %Async NP NS \n",
"0.0 1 0.0 2 2 0.199702 1.0 224.0 1.0\n",
" 10 0.199709 1.0 224.0 1.0\n",
" 20 0.199704 1.0 224.0 1.0\n",
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"... ... ... ... ...\n",
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"\n",
"[288 rows x 4 columns]"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grouped_aggL"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TIEMPO EJECUCCION\n",
"Distribución BestFit -------------------------\n",
"Para 2 padres\n",
"EX numC= 2 p = 0.0 Diff = 0.675 Asíncrono\n",
"EX numC= 20 p = 0.012 Diff = 0.139 Síncrono\n",
"Para 10 padres\n",
"Para 20 padres\n",
"EX numC= 2 p = 0.0 Diff = 1.915 Asíncrono\n",
"EX numC= 20 p = 0.013 Diff = 0.705 Síncrono\n",
"EX numC= 40 p = 0.014 Diff = 0.8 Síncrono\n",
"Para 40 padres\n",
"EX numC= 20 p = 0.039 Diff = 0.734 Síncrono\n",
"EX numC= 40 p = 0.005 Diff = 0.368 Síncrono\n",
"Distribución WorstFit -------------------------\n",
"Para 2 padres\n",
"EX numC= 2 p = 0.0 Diff = 0.691 Asíncrono\n",
"Para 10 padres\n",
"EX numC= 2 p = 0.0 Diff = 1.894 Asíncrono\n",
"Para 20 padres\n",
"EX numC= 10 p = 0.022 Diff = 0.513 Síncrono\n",
"EX numC= 40 p = 0.021 Diff = 0.434 Síncrono\n",
"Para 40 padres\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
" if sys.path[0] == '':\n",
"/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
" del sys.path[0]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"EX numC= 20 p = 0.029 Diff = 0.762 Síncrono\n",
"EX numC= 40 p = 0.002 Diff = 0.302 Síncrono\n",
"SINC: 22 || ASINC: 10\n"
]
}
],
"source": [
"print(\"TIEMPO EJECUCCION\")\n",
"sinc = 0\n",
"asinc = 0\n",
"for dist in [1,2]:\n",
" print(\"Distribución \" + dist_names[dist] + \" -------------------------\")\n",
" dist_v = str(dist)+\",\"+str(dist)\n",
" for numP in values:\n",
" print(\"Para \", numP, \" padres\")\n",
" for numC in values:\n",
" #if numP != numC:\n",
" group = str(numP) + \",\" + str(numC)\n",
" v1 = dfG[(dfG[\"%Async\"] == 0.0)][(dfG.Groups == group)][(dfG[\"Dist\"] == dist_v)]['TE']\n",
" v2 = dfG[(dfG[\"%Async\"] == 100.0)][(dfG.Groups == group)][(dfG[\"Dist\"] == dist_v)]['TE']\n",
" res = stats.ttest_ind(v1, v2)\n",
" diff = grouped_aggG['TE'].loc[(dist_v, 0.0, group)] - grouped_aggG['TE'].loc[(dist_v, 100.0, group)]\n",
" if diff > 0:\n",
" mejor = \"Asíncrono\"\n",
" asinc+=1\n",
" else:\n",
" mejor = \"Síncrono\"\n",
" sinc+=1\n",
" \n",
" if res[1] < p_value:\n",
" print(\"EX numC=\", numC, \"p =\", round(res[1],3), \"Diff =\", abs(round(diff,3)), mejor)\n",
"print(\"SINC: \" + str(sinc) + \" || ASINC: \" + str(asinc))"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TIEMPO MALLEABILITY\n",
"Distribución BestFit -------------------------\n",
"Para 2 padres\n",
"TR numC= 2 p = 0.0 Diff = 0.143 Síncrono\n",
"Para 10 padres\n",
"TR numC= 10 p = 0.0 Diff = 0.05 Síncrono\n",
"TR numC= 20 p = 0.03 Diff = 0.087 Síncrono\n",
"Para 20 padres\n",
"TR numC= 10 p = 0.02 Diff = 0.531 Síncrono\n",
"TR numC= 20 p = 0.005 Diff = 0.839 Síncrono\n",
"TR numC= 40 p = 0.007 Diff = 0.877 Síncrono\n",
"Para 40 padres\n",
"TR numC= 2 p = 0.0 Diff = 2.23 Síncrono\n",
"TR numC= 10 p = 0.034 Diff = 0.791 Síncrono\n",
"TR numC= 20 p = 0.005 Diff = 1.086 Síncrono\n",
"TR numC= 40 p = 0.0 Diff = 0.453 Síncrono\n",
"Distribución WorstFit -------------------------\n",
"Para 2 padres\n",
"TR numC= 2 p = 0.0 Diff = 0.13 Síncrono\n",
"Para 10 padres\n",
"TR numC= 2 p = 0.011 Diff = 0.116 Síncrono\n",
"TR numC= 10 p = 0.035 Diff = 0.929 Síncrono\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:9: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
" if __name__ == '__main__':\n",
"/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
" # Remove the CWD from sys.path while we load stuff.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"TR numC= 20 p = 0.02 Diff = 0.74 Síncrono\n",
"Para 20 padres\n",
"TR numC= 2 p = 0.008 Diff = 3.713 Síncrono\n",
"TR numC= 10 p = 0.001 Diff = 1.051 Síncrono\n",
"TR numC= 20 p = 0.028 Diff = 0.398 Síncrono\n",
"TR numC= 40 p = 0.037 Diff = 0.521 Síncrono\n",
"Para 40 padres\n",
"TR numC= 10 p = 0.04 Diff = 1.07 Síncrono\n",
"TR numC= 20 p = 0.001 Diff = 0.944 Síncrono\n",
"TR numC= 40 p = 0.001 Diff = 0.443 Síncrono\n"
]
}
],
"source": [
"print(\"TIEMPO MALLEABILITY\")\n",
"for dist in [1,2]:\n",
" print(\"Distribución \" + dist_names[dist] + \" -------------------------\")\n",
" dist_v = str(dist)+\",\"+str(dist)\n",
" for numP in values:\n",
" print(\"Para \", numP, \" padres\")\n",
" for numC in values:\n",
" #if numP != numC:\n",
" v1 = dfM[(dfM[\"%Async\"] == 0.0)][(dfM.NP == numP)][(dfM.NS == numC)][(dfM[\"Dist\"] == dist_v)]['TS']\n",
" v2 = dfM[(dfM[\"%Async\"] == 100.0)][(dfM.NP == numP)][(dfM.NS == numC)][(dfM[\"Dist\"] == dist_v)]['TA']\n",
" res = stats.ttest_ind(v1, v2)\n",
" diff = grouped_aggM['TS'].loc[(dist_v, 0.0, numP, numC)] - grouped_aggM['TA'].loc[(dist_v, 100.0, numP, numC)]\n",
" if diff > 0:\n",
" mejor = \"Asíncrono\"\n",
" else:\n",
" mejor = \"Síncrono\"\n",
" if res[1] < p_value:\n",
" print(\"TR numC=\", numC, \"p =\", round(res[1],3), \"Diff =\", abs(round(diff,3)), mejor)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TIEMPO Iters\n",
"Distribución BestFit -------------------------\n",
"Para 2 padres\n",
"Ti numC= 40 p = 0.02 Diff = 0.0421 Síncrono\n",
"Para 10 padres\n",
"Ti numC= 10 p = 0.012 Diff = 0.0 Síncrono\n",
"Ti numC= 20 p = 0.02 Diff = 0.0 Síncrono\n",
"Ti numC= 40 p = 0.0 Diff = 0.023 Síncrono\n",
"Para 20 padres\n",
"Ti numC= 2 p = 0.011 Diff = 0.0009 Síncrono\n",
"Ti numC= 10 p = 0.0 Diff = 0.0037 Síncrono\n",
"Ti numC= 20 p = 0.0 Diff = 0.0139 Síncrono\n",
"Ti numC= 40 p = 0.0 Diff = 0.0158 Síncrono\n",
"Para 40 padres\n",
"Ti numC= 2 p = 0.0 Diff = 0.0089 Síncrono\n",
"Ti numC= 10 p = 0.0 Diff = 0.0099 Síncrono\n",
"Ti numC= 20 p = 0.0 Diff = 0.0125 Síncrono\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
" if sys.path[0] == '':\n",
"/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
" del sys.path[0]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ti numC= 40 p = 0.0 Diff = 0.0208 Síncrono\n",
"Distribución WorstFit -------------------------\n",
"Para 2 padres\n",
"Ti numC= 20 p = 0.048 Diff = 0.0463 Síncrono\n",
"Ti numC= 40 p = 0.003 Diff = 0.0688 Síncrono\n",
"Para 10 padres\n",
"Ti numC= 10 p = 0.0 Diff = 0.0218 Síncrono\n",
"Ti numC= 20 p = 0.0 Diff = 0.0373 Síncrono\n",
"Ti numC= 40 p = 0.0 Diff = 0.0409 Síncrono\n",
"Para 20 padres\n",
"Ti numC= 2 p = 0.0 Diff = 0.0165 Síncrono\n",
"Ti numC= 10 p = 0.0 Diff = 0.0265 Síncrono\n",
"Ti numC= 20 p = 0.0 Diff = 0.041 Síncrono\n",
"Ti numC= 40 p = 0.0 Diff = 0.0374 Síncrono\n",
"Para 40 padres\n",
"Ti numC= 2 p = 0.0 Diff = 0.0116 Síncrono\n",
"Ti numC= 10 p = 0.0 Diff = 0.0112 Síncrono\n",
"Ti numC= 20 p = 0.0 Diff = 0.0148 Síncrono\n",
"Ti numC= 40 p = 0.0 Diff = 0.0211 Síncrono\n"
]
}
],
"source": [
"print(\"TIEMPO Iters\")\n",
"for dist in [1,2]:\n",
" print(\"Distribución \" + dist_names[dist] + \" -------------------------\")\n",
" dist_v = str(dist)+\",\"+str(dist)\n",
" for numP in values:\n",
" print(\"Para \", numP, \" padres\")\n",
" for numC in values:\n",
" #if numP != numC:\n",
" #exp = dfL[(dfL[\"Tt\"] == 0)][(dfL[\"Dist\"] == 1)][(dfL[\"%Async\"] == 0.0)][(dfL.NP == numP)][(dfL.NS == numC)]\n",
" #TimeOp = exp['Ti'] \n",
" #print(TimeOp)\n",
" v1 = dfL[(dfL[\"Tt\"] == 0)][(dfL[\"Dist\"] == dist)][(dfL[\"%Async\"] == 100.0)][(dfL.NP == numP)][(dfL.NS == numC)]['Ti']\n",
" v2 = dfL[(dfL[\"Tt\"] == 1)][(dfL[\"Dist\"] == dist)][(dfL[\"%Async\"] == 100.0)][(dfL.NP == numP)][(dfL.NS == numC)]['Ti']\n",
" res = stats.ttest_ind(v1, v2, equal_var = False)\n",
" diff = grouped_aggL['Ti'].loc[(0, dist, 0.0, numP, numC)] - grouped_aggL['Ti'].loc[(1, dist, 100.0, numP, numC)]\n",
" if diff > 0:\n",
" mejor = \"Asíncrono\"\n",
" else:\n",
" mejor = \"Síncrono\"\n",
" if res[1] < p_value:\n",
" #and abs(diff) > grouped_aggL['Ti'].loc[(0, dist, 0.0, numP, numC)]\n",
" print(\"Ti numC=\", numC, \"p =\", round(res[1],3), \"Diff =\", abs(round(diff,4)), mejor)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10.0\n"
]
}
],
"source": [
"auxIter = pd.DataFrame(dfM['Iters'].str.split(',',1).tolist(),columns = ['Iters0','Iters1'])\n",
"auxIter['Iters1'] = pd.to_numeric(auxIter['Iters1'], errors='coerce')\n",
"iters = auxIter['Iters1'].mean()\n",
"print(iters)\n"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Distribución BestFit -------------------------\n",
"Para 2 padres\n",
"NC=2 Es mejor Asíncrono con una diff de 0.659\n",
"NC=10 Es mejor Asíncrono con una diff de 0.124\n",
"NC=20 Es mejor Asíncrono con una diff de 0.039\n",
"NC=40 Es mejor Síncrono con una diff de 0.077\n",
"Para 10 padres\n",
"NC=2 Es mejor Asíncrono con una diff de 1.996\n",
"NC=10 Es mejor Asíncrono con una diff de 0.147\n",
"NC=20 Es mejor Asíncrono con una diff de 0.097\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
" # Remove the CWD from sys.path while we load stuff.\n",
"/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
" \n",
"/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:16: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
" app.launch_new_instance()\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"NC=40 Es mejor Síncrono con una diff de 0.16\n",
"Para 20 padres\n",
"NC=2 Es mejor Asíncrono con una diff de 1.938\n",
"NC=10 Es mejor Síncrono con una diff de 0.137\n",
"NC=20 Es mejor Síncrono con una diff de 0.642\n",
"NC=40 Es mejor Síncrono con una diff de 0.775\n",
"Para 40 padres\n",
"NC=2 Es mejor Síncrono con una diff de 0.225\n",
"NC=10 Es mejor Síncrono con una diff de 0.397\n",
"NC=20 Es mejor Síncrono con una diff de 0.889\n",
"NC=40 Es mejor Síncrono con una diff de 0.351\n",
"Distribución WorstFit -------------------------\n",
"Para 2 padres\n",
"NC=2 Es mejor Asíncrono con una diff de 0.671\n",
"NC=10 Es mejor Asíncrono con una diff de 0.132\n",
"NC=20 Es mejor Síncrono con una diff de 0.185\n",
"NC=40 Es mejor Síncrono con una diff de 0.455\n",
"Para 10 padres\n",
"NC=2 Es mejor Asíncrono con una diff de 1.888\n",
"NC=10 Es mejor Síncrono con una diff de 0.536\n",
"NC=20 Es mejor Síncrono con una diff de 0.533\n",
"NC=40 Es mejor Síncrono con una diff de 0.176\n",
"Para 20 padres\n",
"NC=2 Es mejor Síncrono con una diff de 1.71\n",
"NC=10 Es mejor Síncrono con una diff de 0.657\n",
"NC=20 Es mejor Síncrono con una diff de 0.191\n",
"NC=40 Es mejor Síncrono con una diff de 0.416\n",
"Para 40 padres\n",
"NC=2 Es mejor Asíncrono con una diff de 1.02\n",
"NC=10 Es mejor Síncrono con una diff de 0.676\n",
"NC=20 Es mejor Síncrono con una diff de 0.738\n",
"NC=40 Es mejor Síncrono con una diff de 0.338\n"
]
}
],
"source": [
"#iters = dfM['Iters'].mean()\n",
"resultados = [0,0]\n",
"for dist in [1,2]:\n",
" print(\"Distribución \" + dist_names[dist] + \" -------------------------\")\n",
" dist_v = str(dist)+\",\"+str(dist)\n",
" for numP in values:\n",
" print(\"Para \", numP, \" padres\")\n",
" for numC in values:\n",
" #if numP != numC:\n",
" Titer = dfL[(dfL[\"Tt\"] == 0)][(dfL[\"Dist\"] == dist)][(dfL.NP == numC)]['Ti'].mean() #Tiempo por iteracion\n",
" i=0\n",
" for adr in [0.0, 100.0]:\n",
" \n",
" auxExp = dfM[(dfM[\"Dist\"] == dist_v)][(dfM[\"%Async\"] == adr)][(dfM.NP == numP)][(dfM.NS == numC)]\n",
" Tr = auxExp['TS'].mean() + auxExp['TA'].mean() #Tiempo de redistribucion\n",
" M_it = dfL[(dfL[\"Tt\"] == 1)][(dfL[\"Dist\"] == dist)][(dfL[\"%Async\"] == adr)][(dfL.NP == numP)][(dfL.NS == numC)]['Ti'].count()/3 #Iteraciones asincronas\n",
" #No se presupone una diferencia temporal entre iteraciones sincronas y asincronas\n",
" if(M_it > iters):\n",
" M_it = iters\n",
" resultados[i] = (iters - M_it) * Titer + Tr\n",
" i+=1\n",
" #print(M_it)\n",
" #print(Titer)\n",
" #print(iters)\n",
" #print((iters - M_it) * Titer)\n",
" #print(Tr)\n",
" #print(\"End\")\n",
"\n",
" if resultados[0] > resultados[1]:\n",
" mejor = \"Asíncrono\"\n",
" else:\n",
" mejor = \"Síncrono\"\n",
" diff = abs(round(resultados[0] - resultados[1], 3))\n",
" print(\"NC=\"+ str(numC) + \" Es mejor \" + mejor + \" con una diff de \"+ str(diff))\n",
" #TODO Comprobar"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for dist in [1,2]:\n",
" v1 = grouped_aggL.loc[(0,dist,100.0,slice(None))]\n",
" v2 = grouped_aggL.loc[(1,dist,100.0,slice(None))]\n",
" aux_aggL = v1['Ti'].values / v2['Ti'].values\n",
"\n",
" colors = ['r', 'orange', 'g', 'm', 'y']\n",
" markers = ['+', 'x', '1', '2', 'X']\n",
"\n",
" f=plt.figure(figsize=(10, 7))\n",
" ax1 = f.add_subplot(111)\n",
" plt.xlim(0, max(values)+1)\n",
" plt.ylim(0, 1.2)\n",
" plt.xticks(values)\n",
" ax1.set_ylabel('Aumento de velocidad')\n",
" ax1.set_xlabel('Procesos hijo')\n",
" ax1.set_title(\"Aumento de velocidad al realizar redistribuciones asíncronas - \" + dist_names[dist])\n",
"\n",
"\n",
" for i in range(len(values)):\n",
" numP = values[i]\n",
" c = colors[i]\n",
" \n",
" mini = i * len(values)\n",
" maxi = (i+1) * len(values)\n",
" array_values = aux_aggL[mini:maxi]\n",
" indexes = np.arange(len(values))\n",
" aux_j=0\n",
" for j in range(len(values)):\n",
" indexes[aux_j] = values[j]\n",
" aux_j+=1\n",
" \n",
" x = indexes\n",
" y = array_values\n",
" label = str(numP) + ' padres'\n",
" ax1.axvline(numP)\n",
" plt.plot(x, y, color=colors[i], label=label, marker=markers[1], markersize=10)\n",
" \n",
" ax1.axhline(1, color='k')\n",
" f.legend()\n",
" #f.tight_layout()\n",
" f.savefig(\"Images/\"+\"Iters\"+ dist_names[dist] +\"_SpeedUp\", format=\"png\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
%% Cell type:code id: tags:
``` python
%matplotlib inline
import pandas as pd
from pandas import DataFrame, Series
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats
import sys
```
%% Cell type:code id: tags:
``` python
matrixMalEX="data_GG.csv"
matrixMal="data_GM.csv"
matrixIt="data_L.csv"
n_qty=2 #CAMBIAR SEGUN LA CANTIDAD DE NODOS USADOS
repet = 3 * 2 #CAMBIAR EL PRIMER NUMERO SEGUN NUMERO DE EJECUCIONES POR CONFIG
p_value = 0.05
values = [2, 10, 20, 40]
dist_names = ['null', 'BestFit', 'WorstFit']
```
%% Cell type:code id: tags:
``` python
def speedUp(arr, seq, df):
numP = df.loc[arr.index[0]].NP
return seq[( seq.NP == numP )]['EX'] / arr.mean()
```
%% Cell type:code id: tags:
``` python
dfG = pd.read_csv( matrixMalEX )
dfG = dfG.drop(columns=dfG.columns[0])
dfG['S'] = dfG['N']
dfG['N'] = dfG['S'] + dfG['%Async']
dfG['%Async'] = (dfG['%Async'] / dfG['N']) * 100
if(n_qty == 1):
group = dfG.groupby(['%Async', 'Groups'])['TE']
else:
group = dfG.groupby(['Dist', '%Async', 'Groups'])['TE']
#group
grouped_aggG = group.agg(['mean'])
grouped_aggG.rename(columns={'mean':'TE',}, inplace=True)
```
%% Cell type:code id: tags:
``` python
dfM = pd.read_csv( matrixMal )
dfM = dfM.drop(columns=dfM.columns[0])
dfM['S'] = dfM['N']
dfM['N'] = dfM['S'] + dfM['%Async']
dfM["TR"] = dfM["TC"] + dfM["TS"] + dfM["TA"]
dfM['%Async'] = (dfM['%Async'] / dfM['N']) * 100
if(n_qty == 1):
groupM = dfM.groupby(['%Async','NP', 'NS'])['TC', 'TS', 'TA', 'TR']
else:
groupM = dfM.groupby(['Dist', '%Async','NP', 'NS'])['TC', 'TS', 'TA', 'TR']
#group
grouped_aggM = groupM.agg(['mean'])
grouped_aggM.columns = grouped_aggM.columns.get_level_values(0)
```
%% Cell type:code id: tags:
``` python
dfL = pd.read_csv( matrixIt )
dfL = dfL.drop(columns=dfL.columns[0])
if(n_qty == 1):
groupL = dfL[dfL['NS'] != 0].groupby(['Tt', '%Async', 'NP', 'NS'])['Ti', 'To']
else:
groupL = dfL[dfL['NS'] != 0].groupby(['Tt', 'Dist', '%Async', 'NP', 'NS'])['Ti', 'To']
#group
grouped_aggL = groupL.agg(['mean', 'count'])
grouped_aggL.columns = grouped_aggL.columns.get_level_values(0)
grouped_aggL.set_axis(['Ti', 'Iters', 'To', 'Iters2'], axis='columns')
grouped_aggL['Iters'] = np.ceil(grouped_aggL['Iters']/6) # TODO Cambiar a repeticiones realizadas
grouped_aggL['Iters2'] = np.ceil(grouped_aggL['Iters2']/6)
```
%%%% Output: stream
/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: FutureWarning: set_axis currently defaults to operating inplace.
This will change in a future version of pandas, use inplace=True to avoid this warning.
if sys.path[0] == '':
%% Cell type:code id: tags:
``` python
grouped_aggL.to_excel("resultL.xlsx")
grouped_aggM.to_excel("resultM.xlsx")
grouped_aggG.to_excel("resultG.xlsx")
```
%% Cell type:code id: tags:
``` python
dfG
```
%%%% Output: execute_result
N %Async Groups Dist Matrix CommTam Time Iters TE \
0 2000000000 0.0 20,40 2,2 100000 0 0.2 1,10 3.325915
1 2000000000 0.0 20,40 2,2 100000 0 0.2 1,10 3.661673
2 2000000000 0.0 20,40 2,2 100000 0 0.2 1,10 3.647241
3 2000000000 75.0 10,40 1,1 100000 0 0.2 1,10 2.710496
4 2000000000 75.0 10,40 1,1 100000 0 0.2 1,10 2.785736
.. ... ... ... ... ... ... ... ... ...
475 2000000000 50.0 10,10 2,2 100000 0 0.2 1,10 1.292079
476 2000000000 50.0 10,10 2,2 100000 0 0.2 1,10 1.683571
477 2000000000 50.0 40,20 1,1 100000 0 0.2 1,10 3.107688
478 2000000000 50.0 40,20 1,1 100000 0 0.2 1,10 3.169551
479 2000000000 50.0 40,20 1,1 100000 0 0.2 1,10 3.442101
S
0 2000000000
1 2000000000
2 2000000000
3 500000000
4 500000000
.. ...
475 1000000000
476 1000000000
477 1000000000
478 1000000000
479 1000000000
[480 rows x 10 columns]
%% Cell type:code id: tags:
``` python
grouped_aggG
```
%%%% Output: execute_result
TE
Dist %Async Groups
1,1 0.0 10,10 7.238413
10,2 7.495134
10,20 1.052943
10,40 2.412291
2,10 1.812608
... ...
2,2 100.0 20,40 3.978562
40,10 3.869905
40,2 4.056287
40,20 4.384910
40,40 3.143114
[160 rows x 1 columns]
%% Cell type:code id: tags:
``` python
dfM
```
%%%% Output: execute_result
N %Async NP NS Dist Matrix CommTam Time Iters TC \
0 2000000000 0.0 20 40 2,2 100000 0 0.2 1,10 1.523284
1 2000000000 0.0 20 40 2,2 100000 0 0.2 1,10 1.395780
2 2000000000 0.0 20 40 2,2 100000 0 0.2 1,10 1.515433
3 2000000000 75.0 10 40 1,1 100000 0 0.2 1,10 1.331821
4 2000000000 75.0 10 40 1,1 100000 0 0.2 1,10 1.338515
.. ... ... .. .. ... ... ... ... ... ...
475 2000000000 50.0 10 10 2,2 100000 0 0.2 1,10 0.573135
476 2000000000 50.0 10 10 2,2 100000 0 0.2 1,10 0.568495
477 2000000000 50.0 40 20 1,1 100000 0 0.2 1,10 1.171373
478 2000000000 50.0 40 20 1,1 100000 0 0.2 1,10 1.227744
479 2000000000 50.0 40 20 1,1 100000 0 0.2 1,10 1.101387
TS TA S TR
0 1.571982 0.000000 2000000000 3.095266
1 2.067917 0.000000 2000000000 3.463697
2 1.939858 0.000000 2000000000 3.455291
3 0.217988 1.279915 500000000 2.829724
4 0.268872 1.359864 500000000 2.967251
.. ... ... ... ...
475 0.091322 0.679278 1000000000 1.343735
476 0.096622 1.075340 1000000000 1.740457
477 0.557700 1.843437 1000000000 3.572510
478 0.634888 1.881406 1000000000 3.744038
479 0.695218 2.296932 1000000000 4.093537
[480 rows x 14 columns]
%% Cell type:code id: tags:
``` python
grouped_aggM
```
%%%% Output: execute_result
TC TS TA TR
Dist %Async NP NS
1,1 0.0 2 2 0.246172 0.658124 0.000000 0.904296
10 0.372526 0.846183 0.000000 1.218709
20 0.463237 0.975418 0.000000 1.438655
40 5.897614 1.306502 0.000000 7.204116
10 2 4.623390 0.823443 0.000000 5.446832
... ... ... ... ...
2,2 100.0 20 40 1.464316 0.000000 2.380671 3.844987
40 2 0.482502 0.000000 3.510194 3.992695
10 1.026574 0.000000 2.781301 3.807875
20 1.397894 0.000000 2.814464 4.212358
40 1.445946 0.000000 1.597486 3.043431
[160 rows x 4 columns]
%% Cell type:code id: tags:
``` python
dfL
```
%%%% Output: execute_result
N %Async NP N_par NS Dist Compute_tam Comm_tam Time \
0 2000000000 0.0 40 20 0 2 100000 0 0.2
1 2000000000 0.0 40 20 0 2 100000 0 0.2
2 2000000000 0.0 40 20 0 2 100000 0 0.2
3 2000000000 0.0 40 20 0 2 100000 0 0.2
4 2000000000 0.0 40 20 0 2 100000 0 0.2
... ... ... .. ... .. ... ... ... ...
14951 2000000000 50.0 40 0 20 1 100000 0 0.2
14952 2000000000 50.0 40 0 20 1 100000 0 0.2
14953 2000000000 50.0 40 0 20 1 100000 0 0.2
14954 2000000000 50.0 40 0 20 1 100000 0 0.2
14955 2000000000 50.0 40 0 20 1 100000 0 0.2
Iters Ti Tt To
0 10 0.009826 0.0 11.0
1 10 0.025812 0.0 11.0
2 10 0.009819 0.0 11.0
3 10 0.011071 0.0 11.0
4 10 0.009815 0.0 11.0
... ... ... ... ...
14951 1 0.025818 1.0 11.0
14952 1 0.009812 1.0 11.0
14953 1 0.025813 1.0 11.0
14954 1 0.025810 1.0 11.0
14955 1 0.009820 1.0 11.0
[14956 rows x 13 columns]
%% Cell type:code id: tags:
``` python
grouped_aggL
```
%%%% Output: execute_result
Ti Iters To Iters2
Tt Dist %Async NP NS
0.0 1 0.0 2 2 0.199702 1.0 224.0 1.0
10 0.199709 1.0 224.0 1.0
20 0.199704 1.0 224.0 1.0
40 0.199695 1.0 224.0 1.0
10 2 0.039240 1.0 44.0 1.0
... ... ... ... ...
1.0 2 100.0 20 40 0.057025 21.0 22.0 21.0
40 2 0.021417 81.0 11.0 81.0
10 0.021047 66.0 11.0 66.0
20 0.024609 54.0 11.0 54.0
40 0.030871 26.0 11.0 26.0
[288 rows x 4 columns]
%% Cell type:code id: tags:
``` python
print("TIEMPO EJECUCCION")
sinc = 0
asinc = 0
for dist in [1,2]:
print("Distribución " + dist_names[dist] + " -------------------------")
dist_v = str(dist)+","+str(dist)
for numP in values:
print("Para ", numP, " padres")
for numC in values:
#if numP != numC:
group = str(numP) + "," + str(numC)
v1 = dfG[(dfG["%Async"] == 0.0)][(dfG.Groups == group)][(dfG["Dist"] == dist_v)]['TE']
v2 = dfG[(dfG["%Async"] == 100.0)][(dfG.Groups == group)][(dfG["Dist"] == dist_v)]['TE']
res = stats.ttest_ind(v1, v2)
diff = grouped_aggG['TE'].loc[(dist_v, 0.0, group)] - grouped_aggG['TE'].loc[(dist_v, 100.0, group)]
if diff > 0:
mejor = "Asíncrono"
asinc+=1
else:
mejor = "Síncrono"
sinc+=1
if res[1] < p_value:
print("EX numC=", numC, "p =", round(res[1],3), "Diff =", abs(round(diff,3)), mejor)
print("SINC: " + str(sinc) + " || ASINC: " + str(asinc))
```
%%%% Output: stream
TIEMPO EJECUCCION
Distribución BestFit -------------------------
Para 2 padres
EX numC= 2 p = 0.0 Diff = 0.675 Asíncrono
EX numC= 20 p = 0.012 Diff = 0.139 Síncrono
Para 10 padres
Para 20 padres
EX numC= 2 p = 0.0 Diff = 1.915 Asíncrono
EX numC= 20 p = 0.013 Diff = 0.705 Síncrono
EX numC= 40 p = 0.014 Diff = 0.8 Síncrono
Para 40 padres
EX numC= 20 p = 0.039 Diff = 0.734 Síncrono
EX numC= 40 p = 0.005 Diff = 0.368 Síncrono
Distribución WorstFit -------------------------
Para 2 padres
EX numC= 2 p = 0.0 Diff = 0.691 Asíncrono
Para 10 padres
EX numC= 2 p = 0.0 Diff = 1.894 Asíncrono
Para 20 padres
EX numC= 10 p = 0.022 Diff = 0.513 Síncrono
EX numC= 40 p = 0.021 Diff = 0.434 Síncrono
Para 40 padres
%%%% Output: stream
/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
if sys.path[0] == '':
/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
del sys.path[0]
%%%% Output: stream
EX numC= 20 p = 0.029 Diff = 0.762 Síncrono
EX numC= 40 p = 0.002 Diff = 0.302 Síncrono
SINC: 22 || ASINC: 10
%% Cell type:code id: tags:
``` python
print("TIEMPO MALLEABILITY")
for dist in [1,2]:
print("Distribución " + dist_names[dist] + " -------------------------")
dist_v = str(dist)+","+str(dist)
for numP in values:
print("Para ", numP, " padres")
for numC in values:
#if numP != numC:
v1 = dfM[(dfM["%Async"] == 0.0)][(dfM.NP == numP)][(dfM.NS == numC)][(dfM["Dist"] == dist_v)]['TS']
v2 = dfM[(dfM["%Async"] == 100.0)][(dfM.NP == numP)][(dfM.NS == numC)][(dfM["Dist"] == dist_v)]['TA']
res = stats.ttest_ind(v1, v2)
diff = grouped_aggM['TS'].loc[(dist_v, 0.0, numP, numC)] - grouped_aggM['TA'].loc[(dist_v, 100.0, numP, numC)]
if diff > 0:
mejor = "Asíncrono"
else:
mejor = "Síncrono"
if res[1] < p_value:
print("TR numC=", numC, "p =", round(res[1],3), "Diff =", abs(round(diff,3)), mejor)
```
%%%% Output: stream
TIEMPO MALLEABILITY
Distribución BestFit -------------------------
Para 2 padres
TR numC= 2 p = 0.0 Diff = 0.143 Síncrono
Para 10 padres
TR numC= 10 p = 0.0 Diff = 0.05 Síncrono
TR numC= 20 p = 0.03 Diff = 0.087 Síncrono
Para 20 padres
TR numC= 10 p = 0.02 Diff = 0.531 Síncrono
TR numC= 20 p = 0.005 Diff = 0.839 Síncrono
TR numC= 40 p = 0.007 Diff = 0.877 Síncrono
Para 40 padres
TR numC= 2 p = 0.0 Diff = 2.23 Síncrono
TR numC= 10 p = 0.034 Diff = 0.791 Síncrono
TR numC= 20 p = 0.005 Diff = 1.086 Síncrono
TR numC= 40 p = 0.0 Diff = 0.453 Síncrono
Distribución WorstFit -------------------------
Para 2 padres
TR numC= 2 p = 0.0 Diff = 0.13 Síncrono
Para 10 padres
TR numC= 2 p = 0.011 Diff = 0.116 Síncrono
TR numC= 10 p = 0.035 Diff = 0.929 Síncrono
%%%% Output: stream
/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:9: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
if __name__ == '__main__':
/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
# Remove the CWD from sys.path while we load stuff.
%%%% Output: stream
TR numC= 20 p = 0.02 Diff = 0.74 Síncrono
Para 20 padres
TR numC= 2 p = 0.008 Diff = 3.713 Síncrono
TR numC= 10 p = 0.001 Diff = 1.051 Síncrono
TR numC= 20 p = 0.028 Diff = 0.398 Síncrono
TR numC= 40 p = 0.037 Diff = 0.521 Síncrono
Para 40 padres
TR numC= 10 p = 0.04 Diff = 1.07 Síncrono
TR numC= 20 p = 0.001 Diff = 0.944 Síncrono
TR numC= 40 p = 0.001 Diff = 0.443 Síncrono
%% Cell type:code id: tags:
``` python
print("TIEMPO Iters")
for dist in [1,2]:
print("Distribución " + dist_names[dist] + " -------------------------")
dist_v = str(dist)+","+str(dist)
for numP in values:
print("Para ", numP, " padres")
for numC in values:
#if numP != numC:
#exp = dfL[(dfL["Tt"] == 0)][(dfL["Dist"] == 1)][(dfL["%Async"] == 0.0)][(dfL.NP == numP)][(dfL.NS == numC)]
#TimeOp = exp['Ti']
#print(TimeOp)
v1 = dfL[(dfL["Tt"] == 0)][(dfL["Dist"] == dist)][(dfL["%Async"] == 100.0)][(dfL.NP == numP)][(dfL.NS == numC)]['Ti']
v2 = dfL[(dfL["Tt"] == 1)][(dfL["Dist"] == dist)][(dfL["%Async"] == 100.0)][(dfL.NP == numP)][(dfL.NS == numC)]['Ti']
res = stats.ttest_ind(v1, v2, equal_var = False)
diff = grouped_aggL['Ti'].loc[(0, dist, 0.0, numP, numC)] - grouped_aggL['Ti'].loc[(1, dist, 100.0, numP, numC)]
if diff > 0:
mejor = "Asíncrono"
else:
mejor = "Síncrono"
if res[1] < p_value:
#and abs(diff) > grouped_aggL['Ti'].loc[(0, dist, 0.0, numP, numC)]
print("Ti numC=", numC, "p =", round(res[1],3), "Diff =", abs(round(diff,4)), mejor)
```
%%%% Output: stream
TIEMPO Iters
Distribución BestFit -------------------------
Para 2 padres
Ti numC= 40 p = 0.02 Diff = 0.0421 Síncrono
Para 10 padres
Ti numC= 10 p = 0.012 Diff = 0.0 Síncrono
Ti numC= 20 p = 0.02 Diff = 0.0 Síncrono
Ti numC= 40 p = 0.0 Diff = 0.023 Síncrono
Para 20 padres
Ti numC= 2 p = 0.011 Diff = 0.0009 Síncrono
Ti numC= 10 p = 0.0 Diff = 0.0037 Síncrono
Ti numC= 20 p = 0.0 Diff = 0.0139 Síncrono
Ti numC= 40 p = 0.0 Diff = 0.0158 Síncrono
Para 40 padres
Ti numC= 2 p = 0.0 Diff = 0.0089 Síncrono
Ti numC= 10 p = 0.0 Diff = 0.0099 Síncrono
Ti numC= 20 p = 0.0 Diff = 0.0125 Síncrono
%%%% Output: stream
/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
if sys.path[0] == '':
/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
del sys.path[0]
%%%% Output: stream
Ti numC= 40 p = 0.0 Diff = 0.0208 Síncrono
Distribución WorstFit -------------------------
Para 2 padres
Ti numC= 20 p = 0.048 Diff = 0.0463 Síncrono
Ti numC= 40 p = 0.003 Diff = 0.0688 Síncrono
Para 10 padres
Ti numC= 10 p = 0.0 Diff = 0.0218 Síncrono
Ti numC= 20 p = 0.0 Diff = 0.0373 Síncrono
Ti numC= 40 p = 0.0 Diff = 0.0409 Síncrono
Para 20 padres
Ti numC= 2 p = 0.0 Diff = 0.0165 Síncrono
Ti numC= 10 p = 0.0 Diff = 0.0265 Síncrono
Ti numC= 20 p = 0.0 Diff = 0.041 Síncrono
Ti numC= 40 p = 0.0 Diff = 0.0374 Síncrono
Para 40 padres
Ti numC= 2 p = 0.0 Diff = 0.0116 Síncrono
Ti numC= 10 p = 0.0 Diff = 0.0112 Síncrono
Ti numC= 20 p = 0.0 Diff = 0.0148 Síncrono
Ti numC= 40 p = 0.0 Diff = 0.0211 Síncrono
%% Cell type:code id: tags:
``` python
auxIter = pd.DataFrame(dfM['Iters'].str.split(',',1).tolist(),columns = ['Iters0','Iters1'])
auxIter['Iters1'] = pd.to_numeric(auxIter['Iters1'], errors='coerce')
iters = auxIter['Iters1'].mean()
print(iters)
```
%%%% Output: stream
10.0
%% Cell type:code id: tags:
``` python
#iters = dfM['Iters'].mean()
resultados = [0,0]
for dist in [1,2]:
print("Distribución " + dist_names[dist] + " -------------------------")
dist_v = str(dist)+","+str(dist)
for numP in values:
print("Para ", numP, " padres")
for numC in values:
#if numP != numC:
Titer = dfL[(dfL["Tt"] == 0)][(dfL["Dist"] == dist)][(dfL.NP == numC)]['Ti'].mean() #Tiempo por iteracion
i=0
for adr in [0.0, 100.0]:
auxExp = dfM[(dfM["Dist"] == dist_v)][(dfM["%Async"] == adr)][(dfM.NP == numP)][(dfM.NS == numC)]
Tr = auxExp['TS'].mean() + auxExp['TA'].mean() #Tiempo de redistribucion
M_it = dfL[(dfL["Tt"] == 1)][(dfL["Dist"] == dist)][(dfL["%Async"] == adr)][(dfL.NP == numP)][(dfL.NS == numC)]['Ti'].count()/3 #Iteraciones asincronas
#No se presupone una diferencia temporal entre iteraciones sincronas y asincronas
if(M_it > iters):
M_it = iters
resultados[i] = (iters - M_it) * Titer + Tr
i+=1
#print(M_it)
#print(Titer)
#print(iters)
#print((iters - M_it) * Titer)
#print(Tr)
#print("End")
if resultados[0] > resultados[1]:
mejor = "Asíncrono"
else:
mejor = "Síncrono"
diff = abs(round(resultados[0] - resultados[1], 3))
print("NC="+ str(numC) + " Es mejor " + mejor + " con una diff de "+ str(diff))
#TODO Comprobar
```
%%%% Output: stream
Distribución BestFit -------------------------
Para 2 padres
NC=2 Es mejor Asíncrono con una diff de 0.659
NC=10 Es mejor Asíncrono con una diff de 0.124
NC=20 Es mejor Asíncrono con una diff de 0.039
NC=40 Es mejor Síncrono con una diff de 0.077
Para 10 padres
NC=2 Es mejor Asíncrono con una diff de 1.996
NC=10 Es mejor Asíncrono con una diff de 0.147
NC=20 Es mejor Asíncrono con una diff de 0.097
%%%% Output: stream
/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
# Remove the CWD from sys.path while we load stuff.
/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:16: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
app.launch_new_instance()
%%%% Output: stream
NC=40 Es mejor Síncrono con una diff de 0.16
Para 20 padres
NC=2 Es mejor Asíncrono con una diff de 1.938
NC=10 Es mejor Síncrono con una diff de 0.137
NC=20 Es mejor Síncrono con una diff de 0.642
NC=40 Es mejor Síncrono con una diff de 0.775
Para 40 padres
NC=2 Es mejor Síncrono con una diff de 0.225
NC=10 Es mejor Síncrono con una diff de 0.397
NC=20 Es mejor Síncrono con una diff de 0.889
NC=40 Es mejor Síncrono con una diff de 0.351
Distribución WorstFit -------------------------
Para 2 padres
NC=2 Es mejor Asíncrono con una diff de 0.671
NC=10 Es mejor Asíncrono con una diff de 0.132
NC=20 Es mejor Síncrono con una diff de 0.185
NC=40 Es mejor Síncrono con una diff de 0.455
Para 10 padres
NC=2 Es mejor Asíncrono con una diff de 1.888
NC=10 Es mejor Síncrono con una diff de 0.536
NC=20 Es mejor Síncrono con una diff de 0.533
NC=40 Es mejor Síncrono con una diff de 0.176
Para 20 padres
NC=2 Es mejor Síncrono con una diff de 1.71
NC=10 Es mejor Síncrono con una diff de 0.657
NC=20 Es mejor Síncrono con una diff de 0.191
NC=40 Es mejor Síncrono con una diff de 0.416
Para 40 padres
NC=2 Es mejor Asíncrono con una diff de 1.02
NC=10 Es mejor Síncrono con una diff de 0.676
NC=20 Es mejor Síncrono con una diff de 0.738
NC=40 Es mejor Síncrono con una diff de 0.338
%% Cell type:code id: tags:
``` python
for dist in [1,2]:
v1 = grouped_aggL.loc[(0,dist,100.0,slice(None))]
v2 = grouped_aggL.loc[(1,dist,100.0,slice(None))]
aux_aggL = v1['Ti'].values / v2['Ti'].values
colors = ['r', 'orange', 'g', 'm', 'y']
markers = ['+', 'x', '1', '2', 'X']
f=plt.figure(figsize=(10, 7))
ax1 = f.add_subplot(111)
plt.xlim(0, max(values)+1)
plt.ylim(0, 1.2)
plt.xticks(values)
ax1.set_ylabel('Aumento de velocidad')
ax1.set_xlabel('Procesos hijo')
ax1.set_title("Aumento de velocidad al realizar redistribuciones asíncronas - " + dist_names[dist])
for i in range(len(values)):
numP = values[i]
c = colors[i]
mini = i * len(values)
maxi = (i+1) * len(values)
array_values = aux_aggL[mini:maxi]
indexes = np.arange(len(values))
aux_j=0
for j in range(len(values)):
indexes[aux_j] = values[j]
aux_j+=1
x = indexes
y = array_values
label = str(numP) + ' padres'
ax1.axvline(numP)
plt.plot(x, y, color=colors[i], label=label, marker=markers[1], markersize=10)
ax1.axhline(1, color='k')
f.legend()
#f.tight_layout()
f.savefig("Images/"+"Iters"+ dist_names[dist] +"_SpeedUp", format="png")
```
%%%% Output: display_data
[Hidden Image Output]
%%%% Output: display_data
[Hidden Image Output]
%% Cell type:code id: tags:
``` python
```
Analysis/iterTimes.py 0 → 100644
View file @ a3730724
import sys
import glob
import numpy as np
import pandas as pd
#-----------------------------------------------
def read_file(f, data, it):
compute_tam = 0
comm_tam = 0
sdr = 0
adr = 0
dist = 0
time = 0
recording = False
it_line = 0
aux_it = 0
iters = 0
np = 0
np_par = 0
ns = 0
columnas = ['Titer','Ttype','Top']
#print(f)
for line in f:
lineS = line.split()
if len(lineS) > 1:
if recording and lineS[0].split(':')[0] in columnas: #Record data
aux_it = 0
lineS.pop(0)
if it_line==0:
for observation in lineS:
data.append([None]*13)
data[it+aux_it][0] = sdr
data[it+aux_it][1] = adr
data[it+aux_it][2] = np
data[it+aux_it][3] = np_par
data[it+aux_it][4] = ns
data[it+aux_it][5] = dist
data[it+aux_it][6] = compute_tam
data[it+aux_it][7] = comm_tam
data[it+aux_it][8] = time
data[it+aux_it][9] = iters
data[it+aux_it][10] = float(observation)
aux_it+=1
elif it_line==1:
for observation in lineS:
data[it+aux_it][11] = float(observation)
aux_it+=1
else:
for observation in lineS:
data[it+aux_it][12] = float(observation)
aux_it+=1
it_line += 1
if(it_line % 3 == 0): # Comprobar si se ha terminado de mirar esta ejecucion
recording = False
it_line = 0
it = it + aux_it
if lineS[0] == "Config:":
compute_tam = int(lineS[1].split('=')[1].split(',')[0])
comm_tam = int(lineS[2].split('=')[1].split(',')[0])
sdr = int(lineS[3].split('=')[1].split(',')[0])
adr = int(lineS[4].split('=')[1].split(',')[0])
time = float(lineS[6].split('=')[1])
elif lineS[0] == "Config":
recording = True
iters = int(lineS[2].split('=')[1].split(',')[0])
dist = int(lineS[4].split('=')[1].split(',')[0])
np = int(lineS[5].split('=')[1].split(',')[0])
np_par = int(lineS[6].split('=')[1].split(',')[0])
ns = int(float(lineS[7].split('=')[1]))
return it
#-----------------------------------------------
#Config: matrix=1000, sdr=1000000000, adr=0, aib=0 time=2.000000
#Config Group: iters=100, factor=1.000000, phy=2, procs=2, parents=0, sons=4
#Ttype: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
if len(sys.argv) < 2:
print("The files name is missing\nUsage: python3 iterTimes.py resultsName directory csvOutName")
exit(1)
if len(sys.argv) >= 3:
BaseDir = sys.argv[2]
print("Searching in directory: "+ BaseDir)
else:
BaseDir = sys.argv[2]
if len(sys.argv) >= 4:
print("Csv name will be: " + sys.argv[3] + ".csv")
name = sys.argv[3]
else:
name = "data"
insideDir = "Run"
lista = glob.glob("./" + BaseDir + insideDir + "*/" + sys.argv[1]+ "*ID*.o*")
print("Number of files found: "+ str(len(lista)));
it = 0
data = [] #0 #1 #2 #3 #4 #5 #6 #7 #8 #9 #10 #11 #12
columns = ["N", "%Async", "NP", "N_par", "NS", "Dist", "Compute_tam", "Comm_tam", "Time", "Iters", "Ti", "Tt", "To"] #13
for elem in lista:
f = open(elem, "r")
it = read_file(f, data, it)
f.close()
#print(data)
df = pd.DataFrame(data, columns=columns)
df['N'] += df['%Async']
df['%Async'] = (df['%Async'] / df['N']) * 100
df.to_csv(name + '.csv')
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