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Commit 8edd155c authored by Iker Martín Álvarez's avatar Iker Martín Álvarez
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Added Code used to create Plots for the Workshop DYNRESHPC24. In addition, it... 

Added Code used to create Plots for the Workshop DYNRESHPC24. In addition, it has been added the processed data and the results obtained from it
parent 871b5f25
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "603f28cb-7479-47e8-8d8d-b18a620925d1",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"from pandas import DataFrame, Series\n",
"import numpy as np\n",
"import math\n",
"\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.patches as mpatches\n",
"import matplotlib.colors as colors\n",
"from matplotlib.legend_handler import HandlerLine2D, HandlerTuple\n",
"from matplotlib.colors import LinearSegmentedColormap\n",
"from scipy import stats\n",
"import scikit_posthocs as sp\n",
"import sys\n",
"\n",
"from mpl_toolkits.mplot3d import axes3d"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "516a5850-000a-488c-a52d-7051a31b691a",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1.429511096, 0.19593417907, 0.17412488914, 0.14939460301, 0.15755367208]\n"
]
}
],
"source": [
"totn = [1429.511096, 195.93417907, 174.12488914, 149.39460301, 157.55367208]\n",
"processes = [2, 20, 40, 80, 160]\n",
"iters = []\n",
"tot_iters = 1000\n",
"for it_value in totn:\n",
" iters.append(it_value / tot_iters)\n",
"print(iters)\n",
"\n",
"labelsMethods_aux = ['Merge - COLS',\n",
" 'Merge - COLA',\n",
" 'Ideal']"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "d1f3ec22-9445-414e-8acf-40da5209a90a",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"#OFI REDUCCION\n",
"\n",
"#CON BARRIERS\n",
"malls = [1.191402, 3.13309, 2.585917, 2.569179, 0.777284, 1.033414, 0.920364, 0.668779, 0.519809, 0.334906]\n",
"malla = [1.612513, 3.265418, 2.965351, 2.98502, 1.002663, 1.030409, 1.112991, 0.873688, 0.871016, 0.727469]\n",
"#SIN BARRIERS\n",
"#malls = []\n",
"#malla = []\n",
"labels_aux = ['(20,2)', '(40,2)', '(80,2)', '(160,2)', '(40,20)', '(80,20)', '(160,20)', '(80,40)', '(160,40)', '(160,80)']\n",
"tuples_aux = [ (20,2), (40,2), (80,2), (160,2), (40,20), (80,20), (160,20), (80,40), (160,40), (160,80)]\n",
"tots = [814.67876291, 808.5604651, 795.9308548, 795.54596305, 190.34062719, 177.72450495, 176.88434005, 163.42062998, 161.76110101, 147.07955885]\n",
"tota = [816.94620895, 807.90405488, 792.34561992, 789.45519304, 183.67259693, 176.26401806, 173.38925004, 159.47613788, 164.16760778, 146.63839984]\n",
"\n",
"spawn_min = [1.45000e-04, 6.29000e-04, 1.05700e-03, 1.47300e-03, 5.85000e-04, 1.04000e-03, 1.42700e-03, 1.04200e-03, 1.53700e-03, 1.44100e-03]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "73b84c51-16da-4277-88cc-a7571d6d159d",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"#OFI EXPANSION\n",
"\n",
"tots = [822.592448, 812.38741612, 687.25450993, 792.73712492, 189.64718318, 180.3323071, 183.42675281, 163.77047706, 156.8667469, 146.14701796]\n",
"tota = [827.57983398, 808.741225, 691.38776207, 798.37592387, 188.47194409, 182.5640409, 177.40146399, 161.12198901, 164.66972589, 150.13075709]\n",
"\n",
"labels_aux = ['(2,20)', '(2,40)', '(2,80)', '(2,160)', '(20,40)', '(20,80)', '(20,160)', '(40,80)', '(40,160)', '(80,160)']\n",
"tuples_aux = [(2,20), (2,40), (2,80), (2,160), (20,40), (20,80), (20,160), (40,80), (40,160), (80,160)]\n",
"#CON BARRIERS\n",
"malls = [1.459048, 1.377933, 1.562658, 1.646107, 1.336804, 1.289738, 1.335803, 1.006473, 1.193941, 1.098027]\n",
"malla = [4.291689, 4.300612, 5.926505, 6.496032, 1.582687, 1.722409, 1.863822, 1.683262, 1.657882, 1.50607]\n",
"#SIN BARRIERS \n",
"#malls = [1.386815, 1.352518, 1.552363, 1.545332, 1.200833, 1.278144, 1.446389, 1.350773, 1.233166, 1.073713]\n",
"#malla = [4.266167, 4.137584, 4.408637, 7.202143, 1.580283, 1.684443, 1.784895, 1.481095, 1.592655, 1.49029]\n",
"\n",
"spawns = [0.646706, 0.637626, 0.79149, 0.874327, 0.703415, 0.77506, 0.923799, 0.922304, 0.875446, 0.808462]\n",
"spawna = [0.667736, 0.599761, 0.883783, 1.069666, 0.683896, 0.838215, 0.875225, 0.769545, 0.918761, 0.887716]\n",
"\n",
"reds_s = [0.031203, 0.034094, 0.036393, 0.036872, 0.325629, 0.161447, 0.051848, 0.275889, 0.117939, 0.124082]\n",
"reds_a = [0.016136, 0.018315, 0.017299, 0.020972, 0.00817, 0.008836, 0.010475, 0.006352, 0.006724, 0.001864]\n",
"\n",
"spawn_min = [5.68926e-01, 5.94807e-01, 6.34480e-01, 7.45979e-01, 5.23472e-01, 6.66369e-01, 8.03722e-01, 6.67325e-01, 8.09060e-01, 7.51335e-01,]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "1d1e1454-5908-4904-b481-cfb21b04c81b",
"metadata": {
"tags": []
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'spawna' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[4], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m index \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(malla)):\n\u001b[1;32m 4\u001b[0m index_iter \u001b[38;5;241m=\u001b[39m index\u001b[38;5;241m%\u001b[39m\u001b[38;5;28mlen\u001b[39m(iters)\n\u001b[0;32m----> 5\u001b[0m mall_aux \u001b[38;5;241m=\u001b[39m malla[index] \u001b[38;5;241m-\u001b[39m spawna[index] \u001b[38;5;241m-\u001b[39m reds_a[index] \n\u001b[1;32m 6\u001b[0m aux_value \u001b[38;5;241m=\u001b[39m math\u001b[38;5;241m.\u001b[39mceil( mall_aux \u001b[38;5;241m/\u001b[39m iters[index_iter])\n\u001b[1;32m 7\u001b[0m aux_value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(aux_value, \u001b[38;5;241m2\u001b[39m)\n",
"\u001b[0;31mNameError\u001b[0m: name 'spawna' is not defined"
]
}
],
"source": [
"iters_a = [] # Total de iteraciones asincronas esperadas\n",
"iters_s = [] # Total de iteraciones sincronas esperadas para caso ideal\n",
"for index in range(len(malla)):\n",
" index_iter = index%len(iters)\n",
" mall_aux = malla[index] - spawna[index] - reds_a[index] \n",
" aux_value = math.ceil( mall_aux / iters[index_iter])\n",
" aux_value = max(aux_value, 2)\n",
" iters_a.append(aux_value)\n",
" \n",
" mall_aux = malls[index] - spawns[index] - reds_s[index] \n",
" aux_value = math.ceil(mall_aux / iters[index_iter])\n",
" aux_value = max(aux_value, 2)\n",
" iters_s.append(aux_value)\n",
"print(iters_a)\n",
"print(iters_s)\n",
"iters_a = np.array(iters_a)\n",
"iters_s = np.array(iters_s)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5e9d9070-70d7-4c30-92eb-4709726ff6c1",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[813.48736091, 805.4273751, 793.3449378, 792.97678405, 189.56334318999998, 176.69109095000002, 175.96397604999999, 162.75185098, 161.24129201, 146.74465285000002]\n",
"[816.94620895, 807.90405488, 792.34561992, 789.45519304, 183.67259693, 176.26401806, 173.38925004, 159.47613788, 164.16760778, 146.63839984]\n"
]
}
],
"source": [
"ideal = []\n",
"for index in range(len(tota)):\n",
" index_iter = index%len(iters)\n",
" aux_value = tots[index] - malls[index]\n",
" ideal.append(aux_value)\n",
"print(ideal)\n",
"print(tota)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "40a27c2c-3aee-4edc-9136-b13fa4416205",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[812.722637535, 801.81799257, 789.4528495049999, 793.5323840399999, 185.029534105, 172.66439103999997, 176.74392557500002, 161.75974607499998, 165.83928061, 153.474137545]\n"
]
}
],
"source": [
"ideal2 = []\n",
"for tuple_aux in tuples_aux:\n",
" index1 = tuple_aux[0]\n",
" index1 = processes.index(index1)\n",
" index2 = tuple_aux[1]\n",
" index2 = processes.index(index2)\n",
" val_aux = (iters[index1] + iters[index2]) * 500\n",
" ideal2.append(val_aux)\n",
"print(ideal2)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "de3b66fe-8992-48c3-8474-f3aa13a1a770",
"metadata": {
"tags": []
},
"outputs": [
{
"ename": "TypeError",
"evalue": "unsupported operand type(s) for -: 'list' and 'list'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m iters_diff \u001b[38;5;241m=\u001b[39m iters_a \u001b[38;5;241m-\u001b[39m iters_s\n\u001b[1;32m 2\u001b[0m iters_diff\n\u001b[1;32m 4\u001b[0m ideal3 \u001b[38;5;241m=\u001b[39m []\n",
"\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'list' and 'list'"
]
}
],
"source": [
"iters_diff = iters_a - iters_s\n",
"iters_diff\n",
"\n",
"ideal3 = []\n",
"for index in range(len(tota)):\n",
" index_iter = index%len(iters)\n",
" aux_value = tota[index] - malla[index] + (iters_diff[index] * iters[index_iter])\n",
" ideal3.append(aux_value)\n",
"print(ideal3)\n",
"print(tota)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "fd74e4dd-553a-4493-8429-9ca62841ffc6",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4.9999999999999996e-06\n",
"100000000000\n",
"[0.28416491103999997, 0.28464891103999995, 0.28507691103999994, 0.28549291103999996, 0.28460491103999996, 0.28505991103999995, 0.28544691104, 0.28506191103999995, 0.28555691103999997, 0.28546091104]\n"
]
}
],
"source": [
"ideal4 = []\n",
"L = 5 * (10 **-6)\n",
"B = 100 * (10 **9)\n",
"D = 3550186388 * 8\n",
"print(L)\n",
"print(B)\n",
"for spawn_value in spawn_min:\n",
" aux_value = L + D / B\n",
" ideal4.append(aux_value + spawn_value)\n",
"print(ideal4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a6606f9f-fc4a-465f-a603-9acc7b146d5a",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"colors_m = ( \n",
" colors.to_rgba(\"red\"), #MCOLS\n",
" colors.to_rgba(\"blue\"), #MCOLA \n",
" colors.to_rgba(\"red\"), #MRMA1T\n",
" colors.to_rgba(\"mediumseagreen\"), #BCOLA\n",
" colors.to_rgba(\"orange\"), #BCOLA\n",
" )\n",
"f=plt.figure(figsize=(22, 14))\n",
"ax=f.add_subplot(111)\n",
"\n",
"x = np.arange(len(labels_aux))\n",
"plot_index = 0\n",
"#ax.plot(x, tots, color=colors_m[plot_index], linestyle='-', marker= '.', markersize=18, label=\"Sinc\")\n",
"tota_tmp = []\n",
"tots_tmp = []\n",
"ideal_tmp = []\n",
"ideal4_tmp = []\n",
"for index in range(len(tota)):\n",
" tota_tmp.append(ideal2[index] / tota[index])\n",
" tots_tmp.append(ideal2[index] / tots[index])\n",
" ideal_tmp.append(ideal2[index] / ideal[index])\n",
" ideal4_tmp.append(ideal[index] / ideal4[index])\n",
" '.','v','s','p',\n",
"plot_index = 1\n",
"ax.plot(x, tots_tmp, color=colors_m[plot_index], linestyle='--', marker='v', markersize=18, label=\"Sinc\")\n",
"plot_index = 2\n",
"ax.plot(x, tota_tmp, color=colors_m[plot_index], linestyle=':', marker='s', markersize=18, label=\"Asinc\")\n",
"plot_index = 3\n",
"ax.plot(x, ideal_tmp, color=colors_m[plot_index], linestyle='-.', marker='p', markersize=18, label=\"Ideal\")\n",
"plot_index = 4\n",
"#ax.plot(x, ideal4_tmp, color=colors_m[plot_index], linestyle='-.', marker='h', markersize=18, label=\"Ideal4\")\n",
"\n",
"ax.set_xlabel(\"(NS,NT)\", fontsize=36)\n",
"name_legend = \"Difference over Ideal\"\n",
"ax.set_ylabel(name_legend, fontsize=36)\n",
"plt.xticks(x, labels_aux,rotation=90)\n",
"ax.tick_params(axis='both', which='major', labelsize=36)\n",
"ax.tick_params(axis='both', which='minor', labelsize=36)\n",
"plt.legend(loc='best', fontsize=30,ncol=2,framealpha=0.8)\n",
"ax.axhline(y=1, color='black', linestyle='--')\n",
" \n",
"#plt.ylim([0,1.1])\n",
"f.tight_layout()\n",
"f.savefig(\"Images/TEST\"+\".eps\", format=\"eps\", dpi=300)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "f37522d1-d4d8-4d86-a96e-ea0b262e0320",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1.191402, 3.13309, 2.585917, 2.569179, 0.777284, 1.033414, 0.920364, 0.668779, 0.519809, 0.334906]\n",
"[1.612513, 3.265418, 2.965351, 2.98502, 1.002663, 1.030409, 1.112991, 0.873688, 0.871016, 0.727469]\n",
"[0.28416491103999997, 0.28464891103999995, 0.28507691103999994, 0.28549291103999996, 0.28460491103999996, 0.28505991103999995, 0.28544691104, 0.28506191103999995, 0.28555691103999997, 0.28546091104]\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 2200x1400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"colors_m = ( \n",
" colors.to_rgba(\"blue\"), #MCOLA \n",
" colors.to_rgba(\"red\"), #MRMA1T\n",
" colors.to_rgba(\"orange\"), #BCOLA\n",
" )\n",
"f=plt.figure(figsize=(22, 14))\n",
"ax=f.add_subplot(111)\n",
"\n",
"x = np.arange(len(labels_aux))\n",
"plot_index = 0\n",
"#ax.plot(x, tots, color=colors_m[plot_index], linestyle='-', marker= '.', markersize=18, label=\"Sinc\")\n",
"malla_tmp = malla.copy()\n",
"malls_tmp = malls.copy()\n",
"#for index in range(len(tota)):\n",
" #malla_tmp[index] = malla[index] / ideal4[index]\n",
" #malls_tmp[index] = malls[index] / ideal4[index]\n",
" #malla_tmp[index] = ideal4[index] / malla[index]\n",
" #malls_tmp[index] = ideal4[index] / malls[index]\n",
"plot_index = 0\n",
"ax.plot(x, malls_tmp, color=colors_m[plot_index], linestyle='--', marker='v', markersize=18, label=labelsMethods_aux[plot_index])\n",
"plot_index = 1\n",
"ax.plot(x, malla_tmp, color=colors_m[plot_index], linestyle=':', marker='s', markersize=18, label=labelsMethods_aux[plot_index])\n",
"plot_index = 2\n",
"ax.plot(x, ideal4, color=colors_m[plot_index], linestyle='-.', marker='h', markersize=18, label=labelsMethods_aux[plot_index])\n",
"\n",
"ax.set_xlabel(\"(NS,NT)\", fontsize=36)\n",
"name_legend = \"Time(s)\"\n",
"ax.set_ylabel(name_legend, fontsize=36)\n",
"plt.xticks(x, labels_aux,rotation=90)\n",
"ax.tick_params(axis='both', which='major', labelsize=36)\n",
"ax.tick_params(axis='both', which='minor', labelsize=36)\n",
"plt.legend(loc='best', fontsize=30,ncol=2,framealpha=0.8)\n",
"#ax.axhline(y=1, color='black', linestyle='--')\n",
"\n",
"print(malls_tmp)\n",
"print(malla_tmp)\n",
"print(ideal4)\n",
"plt.ylim([0,6.7])\n",
"f.tight_layout()\n",
"f.savefig(\"Images/TEST\"+\".eps\", format=\"eps\", dpi=300)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "c961e35e-20dc-4ede-8618-6b4987f2abac",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 80686760\n",
"3404950172\n"
]
}
],
"source": [
"row = 2017169\n",
"matrix = 283073458\n",
"\n",
"mam = row * 8 * 5\n",
"user = (matrix * (4 + 8)) + (row * 4)\n",
"print(\" \" +str(mam))\n",
"print(user)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "4921dc40-d2e1-4f95-941f-79153d1a9e34",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"42.19961455881981"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"user/mam"
]
},
{
"cell_type": "code",
"execution_count": 81,
"id": "a2f35195-aaf1-4dc6-85ff-a612f37b897b",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"3.550186388"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"3550186388 / (1000 * 1000 * 1000)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "ffc12e7f-12f6-46bf-a399-c9c1d231ca9e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-3.32100894, -3.32565943, -4.75105871, -4.70867003, -0.30450212,\n",
" -0.45525678, -0.48542673, -0.71140234, -0.42443466, -0.3941093 ])"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"testa = np.array(malla_tmp)\n",
"tests = np.array(malls_tmp)\n",
"tests- testa"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0d9db153-773f-4690-8699-f37e5678d2a7",
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%% Cell type:code id:603f28cb-7479-47e8-8d8d-b18a620925d1 tags:
``` python
%matplotlib inline
import pandas as pd
from pandas import DataFrame, Series
import numpy as np
import math
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import matplotlib.colors as colors
from matplotlib.legend_handler import HandlerLine2D, HandlerTuple
from matplotlib.colors import LinearSegmentedColormap
from scipy import stats
import scikit_posthocs as sp
import sys
from mpl_toolkits.mplot3d import axes3d
```
%% Cell type:code id:516a5850-000a-488c-a52d-7051a31b691a tags:
``` python
totn = [1429.511096, 195.93417907, 174.12488914, 149.39460301, 157.55367208]
processes = [2, 20, 40, 80, 160]
iters = []
tot_iters = 1000
for it_value in totn:
iters.append(it_value / tot_iters)
print(iters)
labelsMethods_aux = ['Merge - COLS',
'Merge - COLA',
'Ideal']
```
%%%% Output: stream
[1.429511096, 0.19593417907, 0.17412488914, 0.14939460301, 0.15755367208]
%% Cell type:code id:d1f3ec22-9445-414e-8acf-40da5209a90a tags:
``` python
#OFI REDUCCION
#CON BARRIERS
malls = [1.191402, 3.13309, 2.585917, 2.569179, 0.777284, 1.033414, 0.920364, 0.668779, 0.519809, 0.334906]
malla = [1.612513, 3.265418, 2.965351, 2.98502, 1.002663, 1.030409, 1.112991, 0.873688, 0.871016, 0.727469]
#SIN BARRIERS
#malls = []
#malla = []
labels_aux = ['(20,2)', '(40,2)', '(80,2)', '(160,2)', '(40,20)', '(80,20)', '(160,20)', '(80,40)', '(160,40)', '(160,80)']
tuples_aux = [ (20,2), (40,2), (80,2), (160,2), (40,20), (80,20), (160,20), (80,40), (160,40), (160,80)]
tots = [814.67876291, 808.5604651, 795.9308548, 795.54596305, 190.34062719, 177.72450495, 176.88434005, 163.42062998, 161.76110101, 147.07955885]
tota = [816.94620895, 807.90405488, 792.34561992, 789.45519304, 183.67259693, 176.26401806, 173.38925004, 159.47613788, 164.16760778, 146.63839984]
spawn_min = [1.45000e-04, 6.29000e-04, 1.05700e-03, 1.47300e-03, 5.85000e-04, 1.04000e-03, 1.42700e-03, 1.04200e-03, 1.53700e-03, 1.44100e-03]
```
%% Cell type:code id:73b84c51-16da-4277-88cc-a7571d6d159d tags:
``` python
#OFI EXPANSION
tots = [822.592448, 812.38741612, 687.25450993, 792.73712492, 189.64718318, 180.3323071, 183.42675281, 163.77047706, 156.8667469, 146.14701796]
tota = [827.57983398, 808.741225, 691.38776207, 798.37592387, 188.47194409, 182.5640409, 177.40146399, 161.12198901, 164.66972589, 150.13075709]
labels_aux = ['(2,20)', '(2,40)', '(2,80)', '(2,160)', '(20,40)', '(20,80)', '(20,160)', '(40,80)', '(40,160)', '(80,160)']
tuples_aux = [(2,20), (2,40), (2,80), (2,160), (20,40), (20,80), (20,160), (40,80), (40,160), (80,160)]
#CON BARRIERS
malls = [1.459048, 1.377933, 1.562658, 1.646107, 1.336804, 1.289738, 1.335803, 1.006473, 1.193941, 1.098027]
malla = [4.291689, 4.300612, 5.926505, 6.496032, 1.582687, 1.722409, 1.863822, 1.683262, 1.657882, 1.50607]
#SIN BARRIERS
#malls = [1.386815, 1.352518, 1.552363, 1.545332, 1.200833, 1.278144, 1.446389, 1.350773, 1.233166, 1.073713]
#malla = [4.266167, 4.137584, 4.408637, 7.202143, 1.580283, 1.684443, 1.784895, 1.481095, 1.592655, 1.49029]
spawns = [0.646706, 0.637626, 0.79149, 0.874327, 0.703415, 0.77506, 0.923799, 0.922304, 0.875446, 0.808462]
spawna = [0.667736, 0.599761, 0.883783, 1.069666, 0.683896, 0.838215, 0.875225, 0.769545, 0.918761, 0.887716]
reds_s = [0.031203, 0.034094, 0.036393, 0.036872, 0.325629, 0.161447, 0.051848, 0.275889, 0.117939, 0.124082]
reds_a = [0.016136, 0.018315, 0.017299, 0.020972, 0.00817, 0.008836, 0.010475, 0.006352, 0.006724, 0.001864]
spawn_min = [5.68926e-01, 5.94807e-01, 6.34480e-01, 7.45979e-01, 5.23472e-01, 6.66369e-01, 8.03722e-01, 6.67325e-01, 8.09060e-01, 7.51335e-01,]
```
%% Cell type:code id:1d1e1454-5908-4904-b481-cfb21b04c81b tags:
``` python
iters_a = [] # Total de iteraciones asincronas esperadas
iters_s = [] # Total de iteraciones sincronas esperadas para caso ideal
for index in range(len(malla)):
index_iter = index%len(iters)
mall_aux = malla[index] - spawna[index] - reds_a[index]
aux_value = math.ceil( mall_aux / iters[index_iter])
aux_value = max(aux_value, 2)
iters_a.append(aux_value)
mall_aux = malls[index] - spawns[index] - reds_s[index]
aux_value = math.ceil(mall_aux / iters[index_iter])
aux_value = max(aux_value, 2)
iters_s.append(aux_value)
print(iters_a)
print(iters_s)
iters_a = np.array(iters_a)
iters_s = np.array(iters_s)
```
%%%% Output: error
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[4], line 5
3 for index in range(len(malla)):
4 index_iter = index%len(iters)
----> 5 mall_aux = malla[index] - spawna[index] - reds_a[index]
6 aux_value = math.ceil( mall_aux / iters[index_iter])
7 aux_value = max(aux_value, 2)
NameError: name 'spawna' is not defined
%% Cell type:code id:5e9d9070-70d7-4c30-92eb-4709726ff6c1 tags:
``` python
ideal = []
for index in range(len(tota)):
index_iter = index%len(iters)
aux_value = tots[index] - malls[index]
ideal.append(aux_value)
print(ideal)
print(tota)
```
%%%% Output: stream
[813.48736091, 805.4273751, 793.3449378, 792.97678405, 189.56334318999998, 176.69109095000002, 175.96397604999999, 162.75185098, 161.24129201, 146.74465285000002]
[816.94620895, 807.90405488, 792.34561992, 789.45519304, 183.67259693, 176.26401806, 173.38925004, 159.47613788, 164.16760778, 146.63839984]
%% Cell type:code id:40a27c2c-3aee-4edc-9136-b13fa4416205 tags:
``` python
ideal2 = []
for tuple_aux in tuples_aux:
index1 = tuple_aux[0]
index1 = processes.index(index1)
index2 = tuple_aux[1]
index2 = processes.index(index2)
val_aux = (iters[index1] + iters[index2]) * 500
ideal2.append(val_aux)
print(ideal2)
```
%%%% Output: stream
[812.722637535, 801.81799257, 789.4528495049999, 793.5323840399999, 185.029534105, 172.66439103999997, 176.74392557500002, 161.75974607499998, 165.83928061, 153.474137545]
%% Cell type:code id:de3b66fe-8992-48c3-8474-f3aa13a1a770 tags:
``` python
iters_diff = iters_a - iters_s
iters_diff
ideal3 = []
for index in range(len(tota)):
index_iter = index%len(iters)
aux_value = tota[index] - malla[index] + (iters_diff[index] * iters[index_iter])
ideal3.append(aux_value)
print(ideal3)
print(tota)
```
%%%% Output: error
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[7], line 1
----> 1 iters_diff = iters_a - iters_s
2 iters_diff
4 ideal3 = []
TypeError: unsupported operand type(s) for -: 'list' and 'list'
%% Cell type:code id:fd74e4dd-553a-4493-8429-9ca62841ffc6 tags:
``` python
ideal4 = []
L = 5 * (10 **-6)
B = 100 * (10 **9)
D = 3550186388 * 8
print(L)
print(B)
for spawn_value in spawn_min:
aux_value = L + D / B
ideal4.append(aux_value + spawn_value)
print(ideal4)
```
%%%% Output: stream
4.9999999999999996e-06
100000000000
[0.28416491103999997, 0.28464891103999995, 0.28507691103999994, 0.28549291103999996, 0.28460491103999996, 0.28505991103999995, 0.28544691104, 0.28506191103999995, 0.28555691103999997, 0.28546091104]
%% Cell type:code id:a6606f9f-fc4a-465f-a603-9acc7b146d5a tags:
``` python
colors_m = (
colors.to_rgba("red"), #MCOLS
colors.to_rgba("blue"), #MCOLA
colors.to_rgba("red"), #MRMA1T
colors.to_rgba("mediumseagreen"), #BCOLA
colors.to_rgba("orange"), #BCOLA
)
f=plt.figure(figsize=(22, 14))
ax=f.add_subplot(111)
x = np.arange(len(labels_aux))
plot_index = 0
#ax.plot(x, tots, color=colors_m[plot_index], linestyle='-', marker= '.', markersize=18, label="Sinc")
tota_tmp = []
tots_tmp = []
ideal_tmp = []
ideal4_tmp = []
for index in range(len(tota)):
tota_tmp.append(ideal2[index] / tota[index])
tots_tmp.append(ideal2[index] / tots[index])
ideal_tmp.append(ideal2[index] / ideal[index])
ideal4_tmp.append(ideal[index] / ideal4[index])
'.','v','s','p',
plot_index = 1
ax.plot(x, tots_tmp, color=colors_m[plot_index], linestyle='--', marker='v', markersize=18, label="Sinc")
plot_index = 2
ax.plot(x, tota_tmp, color=colors_m[plot_index], linestyle=':', marker='s', markersize=18, label="Asinc")
plot_index = 3
ax.plot(x, ideal_tmp, color=colors_m[plot_index], linestyle='-.', marker='p', markersize=18, label="Ideal")
plot_index = 4
#ax.plot(x, ideal4_tmp, color=colors_m[plot_index], linestyle='-.', marker='h', markersize=18, label="Ideal4")
ax.set_xlabel("(NS,NT)", fontsize=36)
name_legend = "Difference over Ideal"
ax.set_ylabel(name_legend, fontsize=36)
plt.xticks(x, labels_aux,rotation=90)
ax.tick_params(axis='both', which='major', labelsize=36)
ax.tick_params(axis='both', which='minor', labelsize=36)
plt.legend(loc='best', fontsize=30,ncol=2,framealpha=0.8)
ax.axhline(y=1, color='black', linestyle='--')
#plt.ylim([0,1.1])
f.tight_layout()
f.savefig("Images/TEST"+".eps", format="eps", dpi=300)
```
%% Cell type:code id:f37522d1-d4d8-4d86-a96e-ea0b262e0320 tags:
``` python
colors_m = (
colors.to_rgba("blue"), #MCOLA
colors.to_rgba("red"), #MRMA1T
colors.to_rgba("orange"), #BCOLA
)
f=plt.figure(figsize=(22, 14))
ax=f.add_subplot(111)
x = np.arange(len(labels_aux))
plot_index = 0
#ax.plot(x, tots, color=colors_m[plot_index], linestyle='-', marker= '.', markersize=18, label="Sinc")
malla_tmp = malla.copy()
malls_tmp = malls.copy()
#for index in range(len(tota)):
#malla_tmp[index] = malla[index] / ideal4[index]
#malls_tmp[index] = malls[index] / ideal4[index]
#malla_tmp[index] = ideal4[index] / malla[index]
#malls_tmp[index] = ideal4[index] / malls[index]
plot_index = 0
ax.plot(x, malls_tmp, color=colors_m[plot_index], linestyle='--', marker='v', markersize=18, label=labelsMethods_aux[plot_index])
plot_index = 1
ax.plot(x, malla_tmp, color=colors_m[plot_index], linestyle=':', marker='s', markersize=18, label=labelsMethods_aux[plot_index])
plot_index = 2
ax.plot(x, ideal4, color=colors_m[plot_index], linestyle='-.', marker='h', markersize=18, label=labelsMethods_aux[plot_index])
ax.set_xlabel("(NS,NT)", fontsize=36)
name_legend = "Time(s)"
ax.set_ylabel(name_legend, fontsize=36)
plt.xticks(x, labels_aux,rotation=90)
ax.tick_params(axis='both', which='major', labelsize=36)
ax.tick_params(axis='both', which='minor', labelsize=36)
plt.legend(loc='best', fontsize=30,ncol=2,framealpha=0.8)
#ax.axhline(y=1, color='black', linestyle='--')
print(malls_tmp)
print(malla_tmp)
print(ideal4)
plt.ylim([0,6.7])
f.tight_layout()
f.savefig("Images/TEST"+".eps", format="eps", dpi=300)
```
%%%% Output: stream
The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
%%%% Output: stream
[1.191402, 3.13309, 2.585917, 2.569179, 0.777284, 1.033414, 0.920364, 0.668779, 0.519809, 0.334906]
[1.612513, 3.265418, 2.965351, 2.98502, 1.002663, 1.030409, 1.112991, 0.873688, 0.871016, 0.727469]
[0.28416491103999997, 0.28464891103999995, 0.28507691103999994, 0.28549291103999996, 0.28460491103999996, 0.28505991103999995, 0.28544691104, 0.28506191103999995, 0.28555691103999997, 0.28546091104]
%%%% Output: display_data
[Hidden Image Output]
%% Cell type:code id:c961e35e-20dc-4ede-8618-6b4987f2abac tags:
``` python
row = 2017169
matrix = 283073458
mam = row * 8 * 5
user = (matrix * (4 + 8)) + (row * 4)
print(" " +str(mam))
print(user)
```
%%%% Output: stream
80686760
3404950172
%% Cell type:code id:4921dc40-d2e1-4f95-941f-79153d1a9e34 tags:
``` python
user/mam
```
%%%% Output: execute_result
42.19961455881981
%% Cell type:code id:a2f35195-aaf1-4dc6-85ff-a612f37b897b tags:
``` python
3550186388 / (1000 * 1000 * 1000)
```
%%%% Output: execute_result
3.550186388
%% Cell type:code id:ffc12e7f-12f6-46bf-a399-c9c1d231ca9e tags:
``` python
testa = np.array(malla_tmp)
tests = np.array(malls_tmp)
tests- testa
```
%%%% Output: execute_result
array([-3.32100894, -3.32565943, -4.75105871, -4.70867003, -0.30450212,
-0.45525678, -0.48542673, -0.71140234, -0.42443466, -0.3941093 ])
%% Cell type:code id:0d9db153-773f-4690-8699-f37e5678d2a7 tags:
``` python
```
Exec/Images/LinePlot_Malleability_Expand-OFI.eps 0 → 100644
View file @ 8edd155c
%!PS-Adobe-3.0 EPSF-3.0
%%Title: TEST.eps
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%%CreationDate: Mon May 20 23:37:49 2024
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%%HiResBoundingBox: -486.000000 -108.000000 1098.000000 900.000000
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