148 lines
3.1 KiB
Python
Executable File
148 lines
3.1 KiB
Python
Executable File
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import os, sys
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sys.path.append(os.path.abspath(os.path.dirname(os.path.abspath(__file__))+"/../scripts"))
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import fileinput
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import numpy as np
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import matplotlib as mpl
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show = len(sys.argv) > 1 and sys.argv[1] == 'show'
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if not show:
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mpl.use('pgf')
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import matplotlib.pyplot as plt
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import matplotlib.lines as lines
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from common import set_size, config_plt, get_colors
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config_plt()
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def loadData(fn):
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content = []
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with open(fn) as f:
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content = f.readlines()
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values = {}
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for line in content:
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method, ncores, strtime = [x for x in line.split(':')]
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time = float(strtime)
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if method != "seq":
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time = time/float(ncores)
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if not ncores in values:
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values[ncores] = []
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values[ncores].append(time);
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return values
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def figCores(title, xlabel, xticks, output, datasrcs, error=False):
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fig = plt.figure()
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plt.title(title)
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plt.xlabel(xlabel)
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plt.ylabel(u"temps (s)")
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plt.xticks(xticks)
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nd = len(datasrcs)
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width = .4
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pos = .5-.5*nd
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for f, label in datasrcs:
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values = loadData(f)
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barcolor, errcolor = get_colors(['seq', 'omp', 'gen_omp', 'gen_thread'], label)
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if "1" in values:
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values.pop("1")
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for k, a in values.items():
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x = int(k)
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m = np.mean(a)
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s = 2.5758 * np.std(a) / np.sqrt(len(a)) # confiance 99 %
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x = x + pos*width
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plt.bar(x, m, width=width, color=barcolor, label=label)
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if error:
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plt.errorbar(x, m, s, elinewidth=.8, capsize=1, ecolor=errcolor)
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label=''
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pos = pos + 1
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plt.legend(bbox_to_anchor=(1, 1))
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fig.set_size_inches(set_size(455.24408, .8))
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if not show:
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plt.savefig(output, format='pdf', bbox_inches='tight')
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return plt
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def figSpeedUp(title, xlabel, xticks, output, basetime, datasrcs):
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fig = plt.figure()
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plt.title(title)
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plt.xlabel(xlabel)
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plt.ylabel(u"accélération")
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plt.xticks(xticks)
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nd = len(datasrcs)
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width = .4
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pos = .5-.5*nd
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for f, label in datasrcs:
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values = loadData(f)
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barcolor, errcolor = get_colors(['seq', 'omp', 'gen_omp', 'gen_thread'], label)
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if "1" in values:
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values.pop("1")
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for k, a in values.items():
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x = int(k)
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m = basetime/np.mean(a)
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x = x + pos*width
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plt.bar(x, m, width=width, color=barcolor, label=label)
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label=''
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pos = pos + 1
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plt.legend(bbox_to_anchor=(0.38, 1))
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fig.set_size_inches(set_size(455.24408, .8))
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if not show:
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plt.savefig(output, format='pdf', bbox_inches='tight')
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return plt
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# script
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values = loadData('data_imgpro/rt_cores_seq')
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basetime = np.mean(values["1"])
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plts = []
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plt = figCores(u"", u"nombre de cœurs", [2, 4, 6, 8, 10, 12, 14, 16, 18],
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"plots/rt_cores.pdf", [
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# ['data_imgpro/rt_cores_seq', 'seq'],
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['data_imgpro/rt_cores_gen_omp', 'omp'],
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['data_imgpro/rt_cores_gen_omp', 'gen_omp'],
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['data_imgpro/rt_cores_gen_thread', 'gen_thread'],
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],
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error=False
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)
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plts.append(plt)
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plt = figSpeedUp(u"", u"nombre de cœurs", [2, 4, 6, 8, 10, 12, 14, 16, 18],
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"plots/rt_speedup.pdf", basetime, [
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['data_imgpro/rt_cores_gen_omp', 'omp'],
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['data_imgpro/rt_cores_gen_omp', 'gen_omp'],
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['data_imgpro/rt_cores_gen_thread', 'gen_thread'],
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]
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)
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plts.append(plt)
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if show:
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for plt in plts:
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plt.show()
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