diff options
| -rw-r--r-- | benchmark.py | 46 | ||||
| -rw-r--r-- | mysql.py | 13 |
2 files changed, 46 insertions, 13 deletions
diff --git a/benchmark.py b/benchmark.py index 2d5fc94..a05aa15 100644 --- a/benchmark.py +++ b/benchmark.py @@ -42,13 +42,13 @@ class Benchmark (object): self.requirements = [] def save(self, path=None, verbose=False): - print(self.results) f = path if path else self.name + ".save" if verbose: print("Saving results to:", self.name + ".save") # Pickle can't handle namedtuples so convert the dicts of namedtuples # into lists of dicts. - save_data = {"args" : self.results["args"], "targets" : self.results["targets"]} + save_data = {} + save_data.update(self.results) for target in self.results["targets"]: l = [] for ntuple, measures in self.results[target].items(): @@ -63,11 +63,9 @@ class Benchmark (object): if verbose: print("Loading results from:", self.name + ".save") with open(f, "rb") as f: - save_data = pickle.load(f) + self.results = pickle.load(f) # Build new named tuples - self.results["args"] = save_data["args"] - self.results["targets"] = save_data["targets"] - for target in save_data["targets"]: + for target in self.results["targets"]: d = {} for dic, measures in save_data[target]: d[self.Perm(**dic)] = measures @@ -140,12 +138,37 @@ class Benchmark (object): def parse_chattymalloc_data(self, path="chattymalloc.data"): hist = {} + total = 0 with open(path, "r") as f: for l in f.readlines(): - n = int(l) + total += 1 + try: + n = int(l) + except ValueError: + pass hist[n] = hist.get(n, 0) + 1 + hist["total"] = total return hist + def plot_hist_ascii(self, hist, path): + total = hist["total"] + del(hist["total"]) + bins = {} + bin = 1 + for size in sorted(hist): + if int(size) > bin * 16: + bin += 1 + bins[bin] = bins.get(bin, 0) + hist[size] + hist["total"] = total + + with open(path, "w") as f: + print("Total malloc calls:", total, file=f) + print("Histogram of sizes:", file=f) + for b in sorted(bins): + perc = bins[b]/total*100 + print((b-1)*16, '-', b*16-1, '\t', bins[b], + perc, '%', '*'*int(perc/2), file=f) + def run(self, verbose=False, runs=5): n = len(list(self.iterate_args())) * len(self.targets) i = 0 @@ -165,7 +188,7 @@ class Benchmark (object): for perm in self.iterate_args(): i += 1 - print(i + 1, "of", n, "\r", end='') + print(i, "of", n, "\r", end='') actual_cmd = self.perf_cmd @@ -203,14 +226,17 @@ class Benchmark (object): os.remove("status") if hasattr(self, "process_stdout"): - self.process_stdout(result, res.stdout) + self.process_stdout(result, res.stdout, verbose) # Parse perf output if available if self.perf_cmd != "": csvreader = csv.reader(res.stderr.splitlines(), delimiter=',') for row in csvreader: # Split of the user/kernel space info to be better portable - result[row[2].split(":")[0]] = row[0] + try: + result[row[2].split(":")[0]] = row[0] + except Exception as e: + print("Exception", e, "occured on", row, "for", tname, "and", perm) if run == 1: self.results[tname][perm] = [] @@ -31,7 +31,8 @@ class Benchmark_MYSQL( Benchmark ): # mysqld fails with hoard somehow self.targets = copy.copy(common_targets) - del(self.targets["hoard"]) + if "hoard" in self.targets: + del(self.targets["hoard"]) self.args = {"nthreads" : range(1, multiprocessing.cpu_count() * 4 + 1, 2)} self.cmd = cmd @@ -126,7 +127,7 @@ class Benchmark_MYSQL( Benchmark ): self.server.kill() self.server.wait() - def process_stdout(self, result, stdout): + def process_stdout(self, result, stdout, verbose): result["transactions"] = re.search("transactions:\s*(\d*)", stdout).group(1) result["queries"] = re.search("queries:\s*(\d*)", stdout).group(1) # Latency @@ -189,7 +190,6 @@ class Benchmark_MYSQL( Benchmark ): # linear plot for target in targets: y_vals = [] - print(self.results[target]) for perm in self.iterate_args(): d = [int(m["transactions"]) for m in self.results[target][perm]] y_vals.append(np.mean(d)) @@ -224,10 +224,15 @@ class Benchmark_MYSQL( Benchmark ): # Histogram if "hist" in self.results: for t, h in self.results["hist"].items(): + self.plot_hist_ascii(h, os.path.join(sd, self.name+"."+str(t)+".hist")) #Build up data print(t) d = [] num_discarded = 0 + + total = h["total"] + del(h["total"]) + for size, freq in h.items(): if freq > 5 and size <= 10000: d += [size] * freq @@ -244,6 +249,8 @@ class Benchmark_MYSQL( Benchmark ): plt.savefig(os.path.join(sd, self.name + ".hist." + str(t) + ".png")) plt.clf() + h["total"] = total + # Memusage for target in targets: |