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"""Definition of the larson benchmark"""
import re
from src.benchmark import Benchmark
THROUGHPUT_RE = re.compile("^Throughput =\\s*(?P<throughput>\\d+) operations per second.$")
class BenchmarkLarson(Benchmark):
"""Larson server benchmark
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.
"""
def __init__(self):
self.name = "larson"
# Parameters taken from the paper "Memory Allocation for Long-Running Server
# Applications" from Larson and Krishnan
self.cmd = "larson{binary_suffix} 1 8 {maxsize} 1000 50000 1 {threads}"
self.args = {"maxsize": [64, 512, 1024],
"threads": Benchmark.scale_threads_for_cpus(2)}
self.requirements = ["larson"]
super().__init__()
def process_output(self, result, stdout, stderr, target, perm, verbose):
for line in stdout.splitlines():
res = THROUGHPUT_RE.match(line)
if res:
result["throughput"] = int(res.group("throughput"))
return
def summary(self):
# Plot threads->throughput and maxsize->throughput
self.plot_fixed_arg("{throughput}/1000000",
ylabel="'MOPS/s'",
title="'Larson: ' + arg + ' ' + str(arg_value)",
filepostfix="throughput")
self.plot_fixed_arg("({L1-dcache-load-misses}/{L1-dcache-loads})*100",
ylabel="'l1 cache misses in %'",
title="'Larson cache misses: ' + arg + ' ' + str(arg_value)",
filepostfix="cachemisses")
larson = BenchmarkLarson()
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