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import matplotlib.pyplot as plt
import multiprocessing
import numpy as np
import os
import re
from benchmark import Benchmark
throughput_re = re.compile("^Throughput =\s*(?P<throughput>\d+) operations per second.$")
class Benchmark_Larson( Benchmark ):
def __init__(self):
self.name = "larson"
self.descrition = """This benchmark is courtesy of Paul Larson at Microsoft
Research. It simulates a server: each thread allocates
and deallocates objects, and then transfers some objects
(randomly selected) to other threads to be freed."""
self.cmd = "build/larson{binary_suffix} 1 8 {maxsize} 1000 10000 1 {threads}"
self.args = {
"maxsize" : [8, 32, 64, 128, 256, 512, 1024],
"threads" : range(1, multiprocessing.cpu_count() * 2 + 1)
}
self.requirements = ["build/larson"]
super().__init__()
def process_output(self, result, stdout, stderr, target, perm, verbose):
for l in stdout.splitlines():
res = throughput_re.match(l)
if res:
result["throughput"] = int(res.group("throughput"))
return
print(stdout)
print("no match")
def summary(self, sd=None):
# Speedup thrash
args = self.results["args"]
nthreads = args["threads"]
targets = self.results["targets"]
sd = sd or ""
for arg in args:
loose_arg = [a for a in args if a != arg][0]
for arg_value in args[arg]:
for target in targets:
y_vals = []
for perm in self.iterate_args_fixed({arg : arg_value}, args=args):
d = [m["throughput"] for m in self.results[target][perm]]
y_vals.append(np.mean(d))
x_vals = list(range(1, len(y_vals) + 1))
plt.plot(x_vals, y_vals, marker='.', linestyle='-',
label=target, color=targets[target]["color"])
plt.legend()
plt.xticks(x_vals, args[loose_arg])
plt.xlabel(loose_arg)
plt.ylabel("OPS/s")
plt.title("Larson: " + arg + " " + str(arg_value))
plt.savefig(os.path.join(sd, ".".join([self.name, arg, str(arg_value), "png"])))
plt.clf()
larson = Benchmark_Larson()
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