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from collections import namedtuple
import copy
import csv
import itertools
import os
import pickle
import subprocess
from common_targets import common_targets
class Benchmark (object):
defaults = {
"name" : "default_benchmark",
"description" : "This is the default benchmark description please add your own useful one.",
"perf_cmd" : "perf stat -x, -dd ",
"analyse_cmd" : "memusage -p {} -t ",
"cmd" : "true",
"targets" : common_targets,
}
def __init__(self):
# Set default values
for k in Benchmark.defaults:
if not hasattr(self, k):
setattr(self, k, Benchmark.defaults[k])
# non copy types
if not hasattr(self, "args"):
self.args = {}
self.Perm = namedtuple("Perm", self.args.keys())
if not hasattr(self, "results"):
self.results = {}
self.results["args"] = self.args
self.results["targets"] = self.targets
self.results.update({t : {} for t in self.targets})
if not hasattr(self, "requirements"):
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"]}
for target in self.results["targets"]:
l = []
for ntuple, measures in self.results[target].items():
l.append((ntuple._asdict(), measures))
save_data[target] = l
with open(f, "wb") as f:
pickle.dump(save_data, f)
def load(self, path=None, verbose=False):
f = path if path else self.name + ".save"
if verbose:
print("Loading results from:", self.name + ".save")
with open(f, "rb") as f:
save_data = 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"]:
d = {}
for dic, measures in save_data[target]:
d[self.Perm(**dic)] = measures
self.results[target] = d
def prepare(self, verbose=False):
def is_exe(fpath):
return os.path.isfile(fpath) and os.access(fpath, os.X_OK)
for r in self.requirements:
fpath, fname = os.path.split(r)
if fpath:
if not is_exe(r):
return False
else:
found = False
for path in os.environ["PATH"].split(os.pathsep):
exe_file = os.path.join(path, r)
if is_exe(exe_file):
found = True
if not found:
return False
return True
def iterate_args(self):
"""Return a dict for each possible combination of args"""
arg_names = sorted(self.args.keys())
for p in itertools.product(*[self.args[k] for k in arg_names]):
Perm = namedtuple("Perm", arg_names)
yield Perm(*p)
def iterate_args_fixed(self, fixed):
for p in self.iterate_args():
p_dict = p._asdict()
is_fixed = True
for k in fixed:
if p_dict[k] != fixed[k]:
is_fixed = False
break
if is_fixed:
yield p
def analyse(self, verbose=False):
for perm in self.iterate_args():
perm = perm._asdict()
file_name = self.name + "."
file_name += ".".join([str(x) for x in perm.values()])
file_name += ".memusage"
actual_cmd = self.analyse_cmd.format(file_name + ".png")
if "binary_suffix" in self.cmd:
perm["binary_suffix"] = ""
actual_cmd += self.cmd.format(**perm)
with open(file_name + ".hist", "w") as f:
res = subprocess.run(actual_cmd.split(),
stdout=subprocess.PIPE,
stderr=f,
universal_newlines=True)
if res.returncode != 0:
print(actual_cmd, "failed.")
print("Aborting analysing.")
print("You may look at", file_name + ".hist", "to fix this.")
return
def parse_chattymalloc_data(self, path="chattymalloc.data"):
hist = {}
with open(path, "r") as f:
for l in f.readlines():
n = int(l)
hist[n] = hist.get(n, 0) + 1
return hist
def run(self, verbose=False, runs=5):
n = len(list(self.iterate_args())) * len(self.targets)
i = 0
for run in range(1, runs + 1):
print(str(run) + ". run")
for tname, t in self.targets.items():
if not tname in self.results:
self.results[tname] = {}
os.environ["LD_PRELOAD"] = "build/print_status_on_exit.so "
os.environ["LD_PRELOAD"] += t["LD_PRELOAD"]
if hasattr(self, "pretarget_hook"):
if self.pretarget_hook((tname, t), run, verbose):
return False
for perm in self.iterate_args():
i += 1
print(i + 1, "of", n, "\r", end='')
actual_cmd = self.perf_cmd
perm_dict = perm._asdict()
perm_dict.update(t)
actual_cmd += self.cmd.format(**perm_dict)
res = subprocess.run(actual_cmd.split(),
stderr=subprocess.PIPE,
stdout=subprocess.PIPE,
universal_newlines=True)
if res.returncode != 0:
print("\n" + actual_cmd, "exited with", res.returncode, "for", tname)
print("Aborting Benchmark.")
print("Stdout:\n" + res.stdout)
print("Stderr:\n" + res.stderr)
return False
if "ERROR: ld.so" in res.stderr:
print("\nPreloading of", t["LD_PRELOAD"], "failed for", tname)
print("Stderr:\n" + res.stderr)
print("Aborting Benchmark.")
return False
result = {}
# Read VmHWM from status file. If our benchmark didn't fork
# the first occurance of VmHWM is from our benchmark
with open("status", "r") as f:
for l in f.readlines():
if l.startswith("VmHWM:"):
result["VmHWM"] = l.split()[1]
break
os.remove("status")
if hasattr(self, "process_stdout"):
self.process_stdout(result, res.stdout)
# 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]
if run == 1:
self.results[tname][perm] = []
self.results[tname][perm].append(result)
if hasattr(self, "posttarget_hook"):
if self.posttarget_hook((tname, t), run, verbose):
return False
print()
return True
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