1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
|
# Copyright 2018-2019 Florian Fischer <florian.fl.fischer@fau.de>
#
# This file is part of allocbench.
#
# allocbench is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# allocbench is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with allocbench. If not, see <http://www.gnu.org/licenses/>.
"""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):
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__(name)
@staticmethod
def process_output(result, stdout, stderr, target, perm):
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()
|