thesis version

rtbenchmarks/plot_imgpro

@@ -0,0 +1,147 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import os, sys
+sys.path.append(os.path.abspath(os.path.dirname(os.path.abspath(__file__))+"/../scripts"))
+
+import fileinput
+import numpy as np
+import matplotlib as mpl
+
+show = len(sys.argv) > 1 and sys.argv[1] == 'show'
+if not show:
+	mpl.use('pgf')
+
+import matplotlib.pyplot as plt
+import matplotlib.lines as lines
+from common import set_size, config_plt, get_colors
+
+config_plt()
+
+
+def loadData(fn):
+	content = []
+
+	with open(fn) as f:
+		content = f.readlines()
+
+	values = {}
+	for line in content:
+		method, ncores, strtime = [x for x in line.split(':')]
+		time = float(strtime)
+		if method != "seq":
+			time = time/float(ncores)
+		if not ncores in values:
+			values[ncores] = []
+		values[ncores].append(time);
+
+	return values
+
+
+def figCores(title, xlabel, xticks, output, datasrcs, error=False):
+	fig = plt.figure()
+
+	plt.title(title)
+	plt.xlabel(xlabel)
+	plt.ylabel(u"temps (s)")
+	plt.xticks(xticks)
+
+	nd = len(datasrcs)
+
+	width = .4
+	pos = .5-.5*nd
+	for f, label in datasrcs:
+		values = loadData(f)
+		barcolor, errcolor = get_colors(['seq', 'omp', 'gen_omp', 'gen_thread'], label)
+
+		if "1" in values:
+			values.pop("1")
+
+		for k, a in values.items():
+			x = int(k)
+			m = np.mean(a)
+			s = 2.5758 * np.std(a) / np.sqrt(len(a))  # confiance 99 %
+
+			x = x + pos*width
+
+			plt.bar(x, m, width=width, color=barcolor, label=label)
+			if error:
+				plt.errorbar(x, m, s, elinewidth=.8, capsize=1, ecolor=errcolor)
+			label=''
+
+		pos = pos + 1
+
+	plt.legend(bbox_to_anchor=(1, 1))
+
+	fig.set_size_inches(set_size(455.24408, .8))
+	if not show:
+		plt.savefig(output, format='pdf', bbox_inches='tight')
+	return plt
+
+
+def figSpeedUp(title, xlabel, xticks, output, basetime, datasrcs):
+	fig = plt.figure()
+
+	plt.title(title)
+	plt.xlabel(xlabel)
+	plt.ylabel(u"accélération")
+	plt.xticks(xticks)
+
+	nd = len(datasrcs)
+
+	width = .4
+	pos = .5-.5*nd
+	for f, label in datasrcs:
+		values = loadData(f)
+		barcolor, errcolor = get_colors(['seq', 'omp', 'gen_omp', 'gen_thread'], label)
+
+		if "1" in values:
+			values.pop("1")
+
+		for k, a in values.items():
+			x = int(k)
+			m = basetime/np.mean(a)
+
+			x = x + pos*width
+
+			plt.bar(x, m, width=width, color=barcolor, label=label)
+			label=''
+
+		pos = pos + 1
+
+	plt.legend(bbox_to_anchor=(0.38, 1))
+
+	fig.set_size_inches(set_size(455.24408, .8))
+	if not show:
+		plt.savefig(output, format='pdf', bbox_inches='tight')
+	return plt
+
+
+# script
+values = loadData('data_imgpro/rt_cores_seq')
+basetime = np.mean(values["1"])
+plts = []
+
+plt = figCores(u"", u"nombre de cœurs", [2, 4, 6, 8, 10, 12, 14, 16, 18],
+		"plots/rt_cores.pdf", [
+			#  ['data_imgpro/rt_cores_seq', 'seq'],
+			['data_imgpro/rt_cores_gen_omp', 'omp'],
+			['data_imgpro/rt_cores_gen_omp', 'gen_omp'],
+			['data_imgpro/rt_cores_gen_thread', 'gen_thread'],
+			],
+		error=False
+		)
+plts.append(plt)
+
+plt = figSpeedUp(u"", u"nombre de cœurs", [2, 4, 6, 8, 10, 12, 14, 16, 18],
+		"plots/rt_speedup.pdf", basetime, [
+			['data_imgpro/rt_cores_gen_omp', 'omp'],
+			['data_imgpro/rt_cores_gen_omp', 'gen_omp'],
+			['data_imgpro/rt_cores_gen_thread', 'gen_thread'],
+			]
+		)
+plts.append(plt)
+
+if show:
+	for plt in plts:
+		plt.show()