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Diffstat (limited to 'loop.py')
| -rw-r--r-- | loop.py | 155 |
1 files changed, 155 insertions, 0 deletions
@@ -0,0 +1,155 @@ +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.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(): + 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.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.name + "." + str(n) + "threads.png") + plt.clf() + +loop = Benchmark_Loop() |