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import csv
import pickle
import matplotlib.pyplot as plt
import multiprocessing
import numpy as np
import os
import subprocess
from common_targets import common_targets
cmd = ("perf stat -x\; -e cpu-clock:k,cache-references,cache-misses,cycles,"
"instructions,branches,faults,migrations "
"build/memusage build/bench_conprod{0} {1} {1} {1} 1000000 {2}")
class Benchmark_ConProd():
def __init__(self):
self.name = "Consumer Producer Stress Benchmark"
self.descrition = """This benchmark makes 1000000 allocations in each of
n producer threads. The allocations are shared through n
synchronisation objects and freed/consumed by n threads."""
self.targets = common_targets
self.maxsize = [2 ** x for x in range(6, 16)]
self.nthreads = range(1, multiprocessing.cpu_count() + 1)
self.results = {}
def prepare(self, verbose=False):
req = ["build/bench_conprod", "build/memusage"]
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, save=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():
env = {"LD_PRELOAD" : t[1]} if t[1] != "" else None
target_cmd = cmd.format(t[0], *args).split(" ")
if verbose:
print("\n" + tname, t, "\n", " ".join(target_cmd), "\n")
p = subprocess.run(target_cmd, stderr=subprocess.PIPE, stdout=subprocess.PIPE,
env=env, universal_newlines=True)
if p.returncode != 0:
print("\n" + " ".join(target_cmd), "exited with", p.returncode, ".\n Aborting Benchmark.")
print(tname, t)
print(p.stderr)
print(p.stdout)
return False
if "ERROR: ld.so" in p.stderr:
print("\nPreloading of", t[1], "failed for", tname, ".\n Aborting Benchmark.")
return False
output = p.stderr.split("# End memusage\n")
if len(output) != 2:
print()
print(output)
print(tname, t)
print("Aborting output is not correct")
result = {}
# Strip all whitespace from memusage output
result["memusage"] = [x.replace(" ", "").replace("\t", "")
for x in output[0].splitlines()]
# Handle perf output
csvreader = csv.reader(output[1].splitlines(), delimiter=';')
for row in csvreader:
result[row[2].replace("\\", "")] = row[0].replace("\\", "")
key = (tname, *args)
if not key in self.results:
self.results[key] = [result]
else:
self.results[key].append(result)
print()
if save:
with open(self.name + ".save", "wb") as f:
pickle.dump(self.results, f)
return True
def summary(self):
# MAXSIZE fixed
for size in self.maxsize:
for target in self.targets:
y_vals = [0] * len(self.nthreads)
for mid, measures in self.results.items():
if mid[0] == target and mid[2] == size:
d = []
for m in measures:
# nthreads/time = MOPS/S
d.append(mid[1]/float(m["cpu-clock:ku"]))
y_vals[mid[1]-1] = np.mean(d)
plt.plot(self.nthreads, y_vals, label=target)
plt.legend()
plt.xlabel("consumers/producers")
plt.ylabel("MOPS/s")
plt.title("Consumer Producer: " + str(size) + "B")
plt.savefig("Conprod." + str(size) + "B.png")
plt.clf()
# NTHREADS fixed
y_mapping = {v : i for i, v in enumerate(self.maxsize)}
x_vals = [i + 1 for i in range(0, len(self.maxsize))]
for n in self.nthreads:
for target in self.targets:
y_vals = [0] * len(self.maxsize)
for mid, measures in self.results.items():
if mid[0] == target and mid[1] == n:
d = []
for m in measures:
# nthreads/time = MOPS/S
d.append(n/float(m["cpu-clock:ku"]))
y_vals[y_mapping[mid[2]]] = np.mean(d)
plt.plot(x_vals, y_vals, label=target)
plt.legend()
plt.xticks(x_vals, self.maxsize)
plt.xlabel("size in B")
plt.ylabel("MOPS/s")
plt.title("Consumer Producer: " + str(n) + "thread(s)")
plt.savefig("Conprod." + str(n) + "thread.png")
plt.clf()
conprod = Benchmark_ConProd()
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