counter_aggregates = set(['combined', 'first match', 'DS theory',
'no prediction'])
+hot_threshold = 10
def percentage(a, b):
return 100.0 * a / b
values.sort()
return values[int(len(values) / 2)]
+class PredictDefFile:
+ def __init__(self, path):
+ self.path = path
+ self.predictors = {}
+
+ def parse_and_modify(self, heuristics, write_def_file):
+ lines = [x.rstrip() for x in open(self.path).readlines()]
+
+ p = None
+ modified_lines = []
+ for l in lines:
+ if l.startswith('DEF_PREDICTOR'):
+ m = re.match('.*"(.*)".*', l)
+ p = m.group(1)
+ elif l == '':
+ p = None
+
+ if p != None:
+ heuristic = [x for x in heuristics if x.name == p]
+ heuristic = heuristic[0] if len(heuristic) == 1 else None
+
+ m = re.match('.*HITRATE \(([^)]*)\).*', l)
+ if (m != None):
+ self.predictors[p] = int(m.group(1))
+
+ # modify the line
+ if heuristic != None:
+ new_line = (l[:m.start(1)]
+ + str(round(heuristic.get_hitrate()))
+ + l[m.end(1):])
+ l = new_line
+ p = None
+ elif 'PROB_VERY_LIKELY' in l:
+ self.predictors[p] = 100
+ modified_lines.append(l)
+
+ # save the file
+ if write_def_file:
+ with open(self.path, 'w+') as f:
+ for l in modified_lines:
+ f.write(l + '\n')
+class Heuristics:
+ def __init__(self, count, hits, fits):
+ self.count = count
+ self.hits = hits
+ self.fits = fits
+
class Summary:
def __init__(self, name):
self.name = name
- self.branches = 0
- self.successfull_branches = 0
- self.count = 0
- self.hits = 0
- self.fits = 0
+ self.edges= []
+
+ def branches(self):
+ return len(self.edges)
+
+ def hits(self):
+ return sum([x.hits for x in self.edges])
+
+ def fits(self):
+ return sum([x.fits for x in self.edges])
+
+ def count(self):
+ return sum([x.count for x in self.edges])
+
+ def successfull_branches(self):
+ return len([x for x in self.edges if 2 * x.hits >= x.count])
def get_hitrate(self):
- return 100.0 * self.hits / self.count
+ return 100.0 * self.hits() / self.count()
def get_branch_hitrate(self):
- return 100.0 * self.successfull_branches / self.branches
+ return 100.0 * self.successfull_branches() / self.branches()
def count_formatted(self):
- v = self.count
- for unit in ['','K','M','G','T','P','E','Z']:
+ v = self.count()
+ for unit in ['', 'k', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y']:
if v < 1000:
return "%3.2f%s" % (v, unit)
v /= 1000.0
return "%.1f%s" % (v, 'Y')
- def print(self, branches_max, count_max):
+ def count(self):
+ return sum([x.count for x in self.edges])
+
+ def print(self, branches_max, count_max, predict_def):
+ # filter out most hot edges (if requested)
+ self.edges = sorted(self.edges, reverse = True, key = lambda x: x.count)
+ if args.coverage_threshold != None:
+ threshold = args.coverage_threshold * self.count() / 100
+ edges = [x for x in self.edges if x.count < threshold]
+ if len(edges) != 0:
+ self.edges = edges
+
+ predicted_as = None
+ if predict_def != None and self.name in predict_def.predictors:
+ predicted_as = predict_def.predictors[self.name]
+
print('%-40s %8i %5.1f%% %11.2f%% %7.2f%% / %6.2f%% %14i %8s %5.1f%%' %
- (self.name, self.branches,
- percentage(self.branches, branches_max),
+ (self.name, self.branches(),
+ percentage(self.branches(), branches_max),
self.get_branch_hitrate(),
self.get_hitrate(),
- percentage(self.fits, self.count),
- self.count, self.count_formatted(),
- percentage(self.count, count_max)))
+ percentage(self.fits(), self.count()),
+ self.count(), self.count_formatted(),
+ percentage(self.count(), count_max)), end = '')
+
+ if predicted_as != None:
+ print('%12i%% %5.1f%%' % (predicted_as,
+ self.get_hitrate() - predicted_as), end = '')
+ else:
+ print(' ' * 20, end = '')
+
+ # print details about the most important edges
+ if args.coverage_threshold == None:
+ edges = [x for x in self.edges[:100] if x.count * hot_threshold > self.count()]
+ if args.verbose:
+ for c in edges:
+ r = 100.0 * c.count / self.count()
+ print(' %.0f%%:%d' % (r, c.count), end = '')
+ elif len(edges) > 0:
+ print(' %0.0f%%:%d' % (100.0 * sum([x.count for x in edges]) / self.count(), len(edges)), end = '')
+
+ print()
class Profile:
def __init__(self, filename):
self.heuristics[name] = Summary(name)
s = self.heuristics[name]
- s.branches += 1
- s.count += count
if prediction < 50:
hits = count - hits
remaining = count - hits
- if hits >= remaining:
- s.successfull_branches += 1
+ fits = max(hits, remaining)
- s.hits += hits
- s.fits += max(hits, remaining)
+ s.edges.append(Heuristics(count, hits, fits))
def add_loop_niter(self, niter):
if niter > 0:
self.niter_vector.append(niter)
def branches_max(self):
- return max([v.branches for k, v in self.heuristics.items()])
+ return max([v.branches() for k, v in self.heuristics.items()])
def count_max(self):
- return max([v.count for k, v in self.heuristics.items()])
+ return max([v.count() for k, v in self.heuristics.items()])
- def print_group(self, sorting, group_name, heuristics):
+ def print_group(self, sorting, group_name, heuristics, predict_def):
count_max = self.count_max()
branches_max = self.branches_max()
- sorter = lambda x: x.branches
+ sorter = lambda x: x.branches()
if sorting == 'branch-hitrate':
sorter = lambda x: x.get_branch_hitrate()
elif sorting == 'hitrate':
elif sorting == 'name':
sorter = lambda x: x.name.lower()
- print('%-40s %8s %6s %12s %18s %14s %8s %6s' %
+ print('%-40s %8s %6s %12s %18s %14s %8s %6s %12s %6s %s' %
('HEURISTICS', 'BRANCHES', '(REL)',
- 'BR. HITRATE', 'HITRATE', 'COVERAGE', 'COVERAGE', '(REL)'))
+ 'BR. HITRATE', 'HITRATE', 'COVERAGE', 'COVERAGE', '(REL)',
+ 'predict.def', '(REL)', 'HOT branches (>%d%%)' % hot_threshold))
for h in sorted(heuristics, key = sorter):
- h.print(branches_max, count_max)
+ h.print(branches_max, count_max, predict_def)
def dump(self, sorting):
heuristics = self.heuristics.values()
print('No heuristics available')
return
+ predict_def = None
+ if args.def_file != None:
+ predict_def = PredictDefFile(args.def_file)
+ predict_def.parse_and_modify(heuristics, args.write_def_file)
+
special = list(filter(lambda x: x.name in counter_aggregates,
heuristics))
normal = list(filter(lambda x: x.name not in counter_aggregates,
heuristics))
- self.print_group(sorting, 'HEURISTICS', normal)
+ self.print_group(sorting, 'HEURISTICS', normal, predict_def)
print()
- self.print_group(sorting, 'HEURISTIC AGGREGATES', special)
+ self.print_group(sorting, 'HEURISTIC AGGREGATES', special, predict_def)
if len(self.niter_vector) > 0:
print ('\nLoop count: %d' % len(self.niter_vector)),
parser.add_argument('-s', '--sorting', dest = 'sorting',
choices = ['branches', 'branch-hitrate', 'hitrate', 'coverage', 'name'],
default = 'branches')
+parser.add_argument('-d', '--def-file', help = 'path to predict.def')
+parser.add_argument('-w', '--write-def-file', action = 'store_true',
+ help = 'Modify predict.def file in order to set new numbers')
+parser.add_argument('-c', '--coverage-threshold', type = int,
+ help = 'Ignore edges that have percentage coverage >= coverage-threshold')
+parser.add_argument('-v', '--verbose', action = 'store_true', help = 'Print verbose informations')
args = parser.parse_args()
-profile = Profile(sys.argv[1])
-r = re.compile(' (.*) heuristics( of edge [0-9]*->[0-9]*)?( \\(.*\\))?: (.*)%.*exec ([0-9]*) hit ([0-9]*)')
+profile = Profile(args.dump_file)
loop_niter_str = ';; profile-based iteration count: '
-for l in open(args.dump_file).readlines():
- m = r.match(l)
- if m != None and m.group(3) == None:
- name = m.group(1)
- prediction = float(m.group(4))
- count = int(m.group(5))
- hits = int(m.group(6))
+
+for l in open(args.dump_file):
+ if l.startswith(';;heuristics;'):
+ parts = l.strip().split(';')
+ assert len(parts) == 8
+ name = parts[3]
+ prediction = float(parts[6])
+ count = int(parts[4])
+ hits = int(parts[5])
profile.add(name, prediction, count, hits)
elif l.startswith(loop_niter_str):
projects / thirdparty / gcc.git / blobdiff