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https://github.com/boostorg/histogram.git
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82 lines
2.5 KiB
Python
82 lines
2.5 KiB
Python
import os
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import numpy as np
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import sys
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import re
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import json
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from collections import defaultdict, OrderedDict
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from matplotlib.patches import Rectangle
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from matplotlib.lines import Line2D
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from matplotlib.text import Text
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from matplotlib.font_manager import FontProperties
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import matplotlib.pyplot as plt
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import matplotlib as mpl
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mpl.rcParams.update(mpl.rcParamsDefault)
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data = defaultdict(lambda:[])
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for fn in sys.argv[1:]:
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for bench in json.load(open(fn))["benchmarks"]:
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name = bench["name"]
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time = min(bench["cpu_time"], bench["real_time"])
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m = re.match("fill_([0-9])d<([^>]+)>", name)
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tags = m.group(2).split(", ")
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dim = int(m.group(1))
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label = re.search("fill_([a-z]+).json", fn).group(1)
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dist = tags[0]
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if label == "boost":
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label += "-" + {"dynamic_tag":"D", "static_tag":"S"}[tags[1]] + tags[2][0]
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data[dim].append((label, dist, time / dim))
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plt.figure()
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if os.path.exists("/proc/cpuinfo"):
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cpuinfo = open("/proc/cpuinfo").read()
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m = re.search("model name\s*:\s*(.+)\n", cpuinfo)
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if m:
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plt.title(m.group(1))
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i = 0
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for dim in sorted(data):
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v = data[dim]
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labels = OrderedDict()
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for label, dist, time in v:
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if label in labels:
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labels[label][dist] = time
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else:
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labels[label] = {dist: time}
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j = 0
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for label, d in labels.items():
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t1 = d["uniform"]
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t2 = d["normal"]
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i -= 1
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z = float(j) / len(labels)
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col = ((1.0-z) * np.array((1.0, 0.0, 0.0))
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+ z * np.array((1.0, 1.0, 0.0)))
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if label == "root":
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col = "k"
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if "numpy" in label:
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col = "0.6"
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if "gsl" in label:
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col = "0.3"
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tmin = min(t1, t2)
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tmax = max(t1, t2)
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r1 = Rectangle((0, i), tmin, 1, facecolor=col)
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r2 = Rectangle((tmin, i+0.5), tmax-tmin, 0.5, facecolor=col)
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plt.gca().add_artist(r1)
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plt.gca().add_artist(r2)
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tx = Text(-0.1, i+0.5, "%s" % label,
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va="center", ha="right", clip_on=False)
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plt.gca().add_artist(tx)
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j += 1
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i -= 1
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font0 = FontProperties()
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font0.set_weight("bold")
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tx = Text(-0.1, i+0.6, "%iD" % dim,
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fontproperties=font0, va="center", ha="right", clip_on=False)
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plt.gca().add_artist(tx)
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plt.ylim(0, i)
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plt.xlim(0, 20)
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plt.tick_params("y", left=False, labelleft=False)
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plt.xlabel("fill time per random input value in nanoseconds (smaller is better)")
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plt.savefig("fill_performance.svg")
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plt.show()
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