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Diffstat (limited to 'bench_loop.py')
| -rw-r--r-- | bench_loop.py | 156 |
1 files changed, 0 insertions, 156 deletions
diff --git a/bench_loop.py b/bench_loop.py deleted file mode 100644 index 6caedb8..0000000 --- a/bench_loop.py +++ /dev/null @@ -1,156 +0,0 @@ -import csv -import pickle -import matplotlib.pyplot as plt -import multiprocessing -import numpy as np -import os -import subprocess - -from benchmark import Benchmark -from common_targets import common_targets - -cmd = ("perf stat -x\; -e cpu-clock:k,cache-references,cache-misses,cycles," - "instructions,branches,faults,migrations " - "build/bench_loop{} 1.2 {} 1000000 {} 10") - -class Benchmark_Loop( Benchmark ): - def __init__(self): - self.file_name = "bench_loop" - self.name = "Loop Stress Benchmark" - 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(): - result = {"VSZ": [], "RSS" : []} - - os.environ["LD_PRELOAD"] = t[1] - - target_cmd = cmd.format(t[0], *args).split(" ") - if verbose: - print("\n" + tname, t, "\n", " ".join(target_cmd), "\n") - - p = subprocess.Popen(target_cmd, - env=os.environ, - stderr=subprocess.PIPE, - stdout=subprocess.PIPE, - universal_newlines=True) - - while p.poll() == None: - ps = subprocess.run(["ps", "-F", "--ppid", str(p.pid)], stdout=subprocess.PIPE) - lines = ps.stdout.splitlines() - if len(lines) == 1: # perf hasn't forked yet - continue - tokens = str(lines[1]).split() - result["VSZ"].append(tokens[4]) - result["RSS"].append(tokens[5]) - - p.wait() - - output = p.stderr.read() - - if p.returncode != 0: - print("\n" + " ".join(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[1], "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("\\", "") - key = (tname, *args) - if not key in self.results: - self.results[key] = [result] - else: - self.results[key].append(result) - - print() - return True - - def summary(self): - # MAXSIZE fixed - nthreads = self.results["args"]["nthreads"] - maxsize = self.results["args"]["maxsize"] - targets = self.results["targets"] - - y_mapping = {v : i for i, v in enumerate(nthreads)} - for size in maxsize: - for target in targets: - y_vals = [0] * len(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[y_mapping[mid[1]]] = np.mean(d) - plt.plot(nthreads, y_vals, marker='.', linestyle='-', label=target) - - plt.legend() - plt.xlabel("threads") - plt.ylabel("MOPS/s") - plt.title("Loop: " + str(size) + "B") - plt.savefig(self.file_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 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, marker='.', linestyle='-', label=target) - - 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(self.file_name + "." + str(n) + "threads.png") - plt.clf() - -loop = Benchmark_Loop() |