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import csv
import pickle
import matplotlib.pyplot as plt
import multiprocessing
import numpy as np
import os
import subprocess
from subprocess import PIPE
from benchmark import Benchmark
from common_targets import common_targets
perf_cmd = ("perf stat -x\; -d -e cpu-clock,cache-references,cache-misses,cycles,"
"instructions,branches,faults,migrations ")
cmd = "build/bench_loop{} 1.2 {} 1000000 {} 10"
class Benchmark_Loop( Benchmark ):
def __init__(self):
self.name = "loop"
self.descrition = """This benchmark makes n allocations in t concurrent threads.
How allocations are freed can be changed with the benchmark
version""",
self.targets = common_targets
self.maxsize = [2 ** x for x in range(6, 16)]
self.nthreads = range(1, multiprocessing.cpu_count() * 2 + 1)
self.results = {"args" : {"nthreads" : self.nthreads, "maxsize": self.maxsize},
"targets" : self.targets}
def prepare(self, verbose=False):
req = ["build/bench_loop"]
for r in req:
if not os.path.isfile(r):
print(r, "not found")
return False
if not os.access(r, os.X_OK):
print(r, "not found")
return False
if verbose:
print(r, "found and executable.")
return True
def run(self, verbose=False, runs=3):
args_permutations = [(x,y) for x in self.nthreads for y in self.maxsize]
n = len(args_permutations)
for run in range(1, runs + 1):
print(str(run) + ". run")
for i, args in enumerate(args_permutations):
print(i + 1, "of", n, "\r", end='')
# run cmd for each target
for tname, t in self.targets.items():
if not tname in self.results:
self.results[tname] = {}
result = {}
os.environ["LD_PRELOAD"] = t["LD_PRELOAD"]
cur_cmd = cmd.format(t["binary_suffix"], *args)
# Collect memory consumtion on first run
if run == 1:
os.environ["LD_PRELOAD"] = "build/print_status_on_exit.so " + os.environ["LD_PRELOAD"]
subprocess.run(cur_cmd.split(), env=os.environ)
with open("status", "r") as f:
for l in f.readlines():
if l.startswith("VmHWM:"):
result["rssmax"] = l.split()[1]
os.environ["LD_PRELOAD"] = t["LD_PRELOAD"]
target_cmd = perf_cmd + cur_cmd
if verbose:
print("\n" + tname, t, "\n", target_cmd, "\n")
p = subprocess.run(target_cmd.split(),
env=os.environ,
stderr=PIPE,
stdout=PIPE,
universal_newlines=True)
output = p.stderr
if p.returncode != 0:
print("\n" + target_cmd, "exited with", p.returncode, ".\n Aborting Benchmark.")
print(tname, t)
print(output)
print(p.stdout)
return False
if "ERROR: ld.so" in output:
print("\nPreloading of", t["LD_PRELOAD"], "failed for", tname, ".\n Aborting Benchmark.")
print(output)
return False
# Handle perf output
csvreader = csv.reader(output.splitlines(), delimiter=';')
for row in csvreader:
result[row[2].replace("\\", "")] = row[0].replace("\\", "")
if not args in self.results[tname]:
self.results[tname][args] = [result]
else:
self.results[tname][args].append(result)
print()
return True
def summary(self, sd=None):
nthreads = self.results["args"]["nthreads"]
maxsize = self.results["args"]["maxsize"]
targets = self.results["targets"]
sd = sd or ""
# MAXSIZE fixed
y_mapping = {v : i for i, v in enumerate(nthreads)}
for size in maxsize:
for target in targets:
y_vals = [0] * len(nthreads)
for margs, measures in [(a, m) for a, m in self.results[target].items() if a[1] == size]:
d = []
for m in measures:
# nthreads/time = MOPS/s
for e in m:
if "cpu-clock" in e:
d.append(margs[0]/float(m[e]))
y_vals[y_mapping[margs[0]]] = np.mean(d)
plt.plot(nthreads, y_vals, marker='.', linestyle='-', label=target, color=targets[target]["color"])
plt.legend()
plt.xlabel("threads")
plt.ylabel("MOPS/s")
plt.title("Loop: " + str(size) + "B")
plt.savefig(os.path.join(sd, self.name + "." + str(size) + "B.png"))
plt.clf()
# NTHREADS fixed
y_mapping = {v : i for i, v in enumerate(maxsize)}
x_vals = [i + 1 for i in range(0, len(maxsize))]
for n in nthreads:
for target in targets:
y_vals = [0] * len(maxsize)
for margs, measures in [(a, m) for a, m in self.results[target].items() if a[0] == n]:
d = []
for m in measures:
# nthreads/time = MOPS/S
for e in m:
if "cpu-clock" in e:
d.append(margs[0]/float(m[e]))
y_vals[y_mapping[margs[1]]] = np.mean(d)
plt.plot(x_vals, y_vals, marker='.', linestyle='-', label=target, color=targets[target]["color"])
plt.legend()
plt.xticks(x_vals, maxsize)
plt.xlabel("size in B")
plt.ylabel("MOPS/s")
plt.title("Loop: " + str(n) + "thread(s)")
plt.savefig(os.path.join(sd, self.name + "." + str(n) + "threads.png"))
plt.clf()
#Memusage
y_mapping = {v : i for i, v in enumerate(nthreads)}
for size in maxsize:
for target in targets:
y_vals = [0] * len(nthreads)
for margs, measures in [(a, m) for a, m in self.results[target].items() if a[1] == size]:
y_vals[y_mapping[margs[0]]] = int(measures[0]["rssmax"])
plt.plot(nthreads, y_vals, marker='.', linestyle='-', label=target, color=targets[target]["color"])
plt.legend()
plt.xlabel("threads")
plt.ylabel("kb")
plt.title("Memusage Loop: " + str(size) + "B")
plt.savefig(os.path.join(sd, self.name + "." + str(size) + "B.mem.png"))
plt.clf()
# NTHREADS fixed
y_mapping = {v : i for i, v in enumerate(maxsize)}
x_vals = [i + 1 for i in range(0, len(maxsize))]
for n in nthreads:
for target in targets:
y_vals = [0] * len(maxsize)
for margs, measures in [(a, m) for a, m in self.results[target].items() if a[0] == n]:
y_vals[y_mapping[margs[1]]] = int(measures[0]["rssmax"])
plt.plot(x_vals, y_vals, marker='.', linestyle='-', label=target, color=targets[target]["color"])
plt.legend()
plt.xticks(x_vals, maxsize)
plt.xlabel("size in B")
plt.ylabel("kb")
plt.title("Memusage Loop: " + str(n) + "thread(s)")
plt.savefig(os.path.join(sd, self.name + "." + str(n) + "threads.mem.png"))
plt.clf()
loop = Benchmark_Loop()
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