from math import *
+counter_aggregates = set(['combined', 'first match', 'DS theory',
+ 'no prediction'])
+
def percentage(a, b):
return 100.0 * a / b
def __init__(self, name):
self.name = name
self.branches = 0
+ self.successfull_branches = 0
self.count = 0
self.hits = 0
self.fits = 0
def get_hitrate(self):
- return self.hits / self.count
+ return 100.0 * self.hits / self.count
+
+ def get_branch_hitrate(self):
+ return 100.0 * self.successfull_branches / self.branches
def count_formatted(self):
v = self.count
v /= 1000.0
return "%.1f%s" % (v, 'Y')
+ def print(self, branches_max, count_max):
+ 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.get_branch_hitrate(),
+ self.get_hitrate(),
+ percentage(self.fits, self.count),
+ self.count, self.count_formatted(),
+ percentage(self.count, count_max)))
+
class Profile:
def __init__(self, filename):
self.filename = filename
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
+
s.hits += hits
- s.fits += max(hits, count - hits)
+ s.fits += max(hits, remaining)
def add_loop_niter(self, niter):
if niter > 0:
def count_max(self):
return max([v.count for k, v in self.heuristics.items()])
- def dump(self, sorting):
- sorter = lambda x: x[1].branches
- if sorting == 'hitrate':
- sorter = lambda x: x[1].get_hitrate()
+ def print_group(self, sorting, group_name, heuristics):
+ count_max = self.count_max()
+ branches_max = self.branches_max()
+
+ sorter = lambda x: x.branches
+ if sorting == 'branch-hitrate':
+ sorter = lambda x: x.get_branch_hitrate()
+ elif sorting == 'hitrate':
+ sorter = lambda x: x.get_hitrate()
elif sorting == 'coverage':
- sorter = lambda x: x[1].count
+ sorter = lambda x: x.count
+ elif sorting == 'name':
+ sorter = lambda x: x.name.lower()
+
+ print('%-40s %8s %6s %12s %18s %14s %8s %6s' %
+ ('HEURISTICS', 'BRANCHES', '(REL)',
+ 'BR. HITRATE', 'HITRATE', 'COVERAGE', 'COVERAGE', '(REL)'))
+ for h in sorted(heuristics, key = sorter):
+ h.print(branches_max, count_max)
+
+ def dump(self, sorting):
+ heuristics = self.heuristics.values()
+ if len(heuristics) == 0:
+ print('No heuristics available')
+ return
+
+ special = list(filter(lambda x: x.name in counter_aggregates,
+ heuristics))
+ normal = list(filter(lambda x: x.name not in counter_aggregates,
+ heuristics))
- print('%-40s %8s %6s %-16s %14s %8s %6s' % ('HEURISTICS', 'BRANCHES', '(REL)',
- 'HITRATE', 'COVERAGE', 'COVERAGE', '(REL)'))
- for (k, v) in sorted(self.heuristics.items(), key = sorter):
- print('%-40s %8i %5.1f%% %6.2f%% / %6.2f%% %14i %8s %5.1f%%' %
- (k, v.branches, percentage(v.branches, self.branches_max ()),
- percentage(v.hits, v.count), percentage(v.fits, v.count),
- v.count, v.count_formatted(), percentage(v.count, self.count_max()) ))
+ self.print_group(sorting, 'HEURISTICS', normal)
+ print()
+ self.print_group(sorting, 'HEURISTIC AGGREGATES', special)
if len(self.niter_vector) > 0:
print ('\nLoop count: %d' % len(self.niter_vector)),
print(' median # of iter: %.2f' % median(self.niter_vector))
for v in [1, 5, 10, 20, 30]:
cut = 0.01 * v
- print(' avg. (%d%% cutoff) # of iter: %.2f' % (v, average_cutoff(self.niter_vector, cut)))
+ print(' avg. (%d%% cutoff) # of iter: %.2f'
+ % (v, average_cutoff(self.niter_vector, cut)))
parser = argparse.ArgumentParser()
-parser.add_argument('dump_file', metavar = 'dump_file', help = 'IPA profile dump file')
-parser.add_argument('-s', '--sorting', dest = 'sorting', choices = ['branches', 'hitrate', 'coverage'], default = 'branches')
+parser.add_argument('dump_file', metavar = 'dump_file',
+ help = 'IPA profile dump file')
+parser.add_argument('-s', '--sorting', dest = 'sorting',
+ choices = ['branches', 'branch-hitrate', 'hitrate', 'coverage', 'name'],
+ default = 'branches')
args = parser.parse_args()
script_location = os.path.realpath(__file__)
parser = argparse.ArgumentParser()
-parser.add_argument('location', metavar = 'dump_file', help = 'Location with SPEC benchmarks')
-parser.add_argument('-s', '--sorting', dest = 'sorting', choices = ['branches', 'hitrate', 'coverage'], default = 'branches')
+parser.add_argument('location', metavar = 'dump_file',
+ help = 'Location with SPEC benchmarks')
+parser.add_argument('-s', '--sorting', dest = 'sorting',
+ choices = ['branches', 'branch-hitrate', 'hitrate', 'coverage', 'name'],
+ default = 'branches')
args = parser.parse_args()
print()
print(b)
sys.stdout.flush()
- p = [os.path.join(os.path.dirname(script_location), 'analyze_brprob.py'), temp.name, '--sorting', args.sorting]
+ p = [os.path.join(os.path.dirname(script_location), 'analyze_brprob.py'),
+ temp.name, '--sorting', args.sorting]
p = subprocess.check_call(p)
sys.stdout.flush()