Fix usage of analyze_brprob.py script.
2018-01-19 Martin Liska <mliska@suse.cz> * analyze_brprob.py: Support new format that can be easily parsed. Add new column to report. 2018-01-19 Martin Liska <mliska@suse.cz> * predict.c (dump_prediction): Add new format for analyze_brprob.py script which is enabled with -details suboption. * profile-count.h (precise_p): New function. From-SVN: r256886
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Martin Liska
Martin Liska
parent
09a7858b2c
commit
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5 changed files with 102 additions and 31 deletions
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@ -1,3 +1,8 @@
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2018-01-19 Martin Liska <mliska@suse.cz>
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* analyze_brprob.py: Support new format that can be easily
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parsed. Add new column to report.
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2018-01-03 Jakub Jelinek <jakub@redhat.com>
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* update-copyright.py: Skip pdt-5.f03 in gfortran.dg subdir.
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@ -71,6 +71,7 @@ from math import *
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counter_aggregates = set(['combined', 'first match', 'DS theory',
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'no prediction'])
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hot_threshold = 10
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def percentage(a, b):
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return 100.0 * a / b
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@ -131,47 +132,87 @@ class PredictDefFile:
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with open(self.path, 'w+') as f:
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for l in modified_lines:
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f.write(l + '\n')
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class Heuristics:
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def __init__(self, count, hits, fits):
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self.count = count
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self.hits = hits
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self.fits = fits
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class Summary:
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def __init__(self, name):
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self.name = name
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self.branches = 0
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self.successfull_branches = 0
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self.count = 0
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self.hits = 0
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self.fits = 0
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self.edges= []
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def branches(self):
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return len(self.edges)
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def hits(self):
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return sum([x.hits for x in self.edges])
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def fits(self):
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return sum([x.fits for x in self.edges])
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def count(self):
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return sum([x.count for x in self.edges])
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def successfull_branches(self):
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return len([x for x in self.edges if 2 * x.hits >= x.count])
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def get_hitrate(self):
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return 100.0 * self.hits / self.count
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return 100.0 * self.hits() / self.count()
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def get_branch_hitrate(self):
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return 100.0 * self.successfull_branches / self.branches
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return 100.0 * self.successfull_branches() / self.branches()
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def count_formatted(self):
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v = self.count
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v = self.count()
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for unit in ['', 'k', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y']:
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if v < 1000:
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return "%3.2f%s" % (v, unit)
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v /= 1000.0
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return "%.1f%s" % (v, 'Y')
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def count(self):
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return sum([x.count for x in self.edges])
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def print(self, branches_max, count_max, predict_def):
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# filter out most hot edges (if requested)
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self.edges = sorted(self.edges, reverse = True, key = lambda x: x.count)
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if args.coverage_threshold != None:
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threshold = args.coverage_threshold * self.count() / 100
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edges = [x for x in self.edges if x.count < threshold]
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if len(edges) != 0:
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self.edges = edges
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predicted_as = None
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if predict_def != None and self.name in predict_def.predictors:
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predicted_as = predict_def.predictors[self.name]
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print('%-40s %8i %5.1f%% %11.2f%% %7.2f%% / %6.2f%% %14i %8s %5.1f%%' %
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(self.name, self.branches,
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percentage(self.branches, branches_max),
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(self.name, self.branches(),
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percentage(self.branches(), branches_max),
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self.get_branch_hitrate(),
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self.get_hitrate(),
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percentage(self.fits, self.count),
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self.count, self.count_formatted(),
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percentage(self.count, count_max)), end = '')
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percentage(self.fits(), self.count()),
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self.count(), self.count_formatted(),
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percentage(self.count(), count_max)), end = '')
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if predicted_as != None:
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print('%12i%% %5.1f%%' % (predicted_as,
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self.get_hitrate() - predicted_as), end = '')
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else:
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print(' ' * 20, end = '')
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# print details about the most important edges
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if args.coverage_threshold == None:
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edges = [x for x in self.edges[:100] if x.count * hot_threshold > self.count()]
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if args.verbose:
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for c in edges:
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r = 100.0 * c.count / self.count()
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print(' %.0f%%:%d' % (r, c.count), end = '')
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elif len(edges) > 0:
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print(' %0.0f%%:%d' % (100.0 * sum([x.count for x in edges]) / self.count(), len(edges)), end = '')
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print()
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class Profile:
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@ -185,33 +226,29 @@ class Profile:
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self.heuristics[name] = Summary(name)
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s = self.heuristics[name]
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s.branches += 1
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s.count += count
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if prediction < 50:
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hits = count - hits
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remaining = count - hits
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if hits >= remaining:
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s.successfull_branches += 1
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fits = max(hits, remaining)
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s.hits += hits
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s.fits += max(hits, remaining)
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s.edges.append(Heuristics(count, hits, fits))
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def add_loop_niter(self, niter):
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if niter > 0:
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self.niter_vector.append(niter)
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def branches_max(self):
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return max([v.branches for k, v in self.heuristics.items()])
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return max([v.branches() for k, v in self.heuristics.items()])
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def count_max(self):
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return max([v.count for k, v in self.heuristics.items()])
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return max([v.count() for k, v in self.heuristics.items()])
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def print_group(self, sorting, group_name, heuristics, predict_def):
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count_max = self.count_max()
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branches_max = self.branches_max()
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sorter = lambda x: x.branches
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sorter = lambda x: x.branches()
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if sorting == 'branch-hitrate':
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sorter = lambda x: x.get_branch_hitrate()
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elif sorting == 'hitrate':
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@ -221,10 +258,10 @@ class Profile:
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elif sorting == 'name':
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sorter = lambda x: x.name.lower()
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print('%-40s %8s %6s %12s %18s %14s %8s %6s %12s %6s' %
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print('%-40s %8s %6s %12s %18s %14s %8s %6s %12s %6s %s' %
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('HEURISTICS', 'BRANCHES', '(REL)',
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'BR. HITRATE', 'HITRATE', 'COVERAGE', 'COVERAGE', '(REL)',
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'predict.def', '(REL)'))
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'predict.def', '(REL)', 'HOT branches (>%d%%)' % hot_threshold))
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for h in sorted(heuristics, key = sorter):
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h.print(branches_max, count_max, predict_def)
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@ -266,19 +303,23 @@ parser.add_argument('-s', '--sorting', dest = 'sorting',
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parser.add_argument('-d', '--def-file', help = 'path to predict.def')
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parser.add_argument('-w', '--write-def-file', action = 'store_true',
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help = 'Modify predict.def file in order to set new numbers')
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parser.add_argument('-c', '--coverage-threshold', type = int,
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help = 'Ignore edges that have percentage coverage >= coverage-threshold')
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parser.add_argument('-v', '--verbose', action = 'store_true', help = 'Print verbose informations')
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args = parser.parse_args()
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profile = Profile(args.dump_file)
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r = re.compile(' (.*) heuristics( of edge [0-9]*->[0-9]*)?( \\(.*\\))?: (.*)%.*exec ([0-9]*) hit ([0-9]*)')
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loop_niter_str = ';; profile-based iteration count: '
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for l in open(args.dump_file):
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m = r.match(l)
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if m != None and m.group(3) == None:
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name = m.group(1)
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prediction = float(m.group(4))
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count = int(m.group(5))
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hits = int(m.group(6))
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if l.startswith(';;heuristics;'):
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parts = l.strip().split(';')
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assert len(parts) == 8
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name = parts[3]
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prediction = float(parts[6])
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count = int(parts[4])
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hits = int(parts[5])
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profile.add(name, prediction, count, hits)
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elif l.startswith(loop_niter_str):
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@ -1,3 +1,10 @@
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2018-01-19 Martin Liska <mliska@suse.cz>
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* predict.c (dump_prediction): Add new format for
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analyze_brprob.py script which is enabled with -details
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suboption.
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* profile-count.h (precise_p): New function.
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2018-01-19 Richard Sandiford <richard.sandiford@linaro.org>
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PR tree-optimization/83922
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@ -747,6 +747,19 @@ dump_prediction (FILE *file, enum br_predictor predictor, int probability,
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}
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fprintf (file, "\n");
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/* Print output that be easily read by analyze_brprob.py script. We are
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interested only in counts that are read from GCDA files. */
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if (dump_file && (dump_flags & TDF_DETAILS)
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&& bb->count.precise_p ()
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&& reason == REASON_NONE)
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{
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gcc_assert (e->count ().precise_p ());
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fprintf (file, ";;heuristics;%s;%" PRId64 ";%" PRId64 ";%.1f;\n",
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predictor_info[predictor].name,
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bb->count.to_gcov_type (), e->count ().to_gcov_type (),
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probability * 100.0 / REG_BR_PROB_BASE);
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}
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}
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/* Return true if STMT is known to be unlikely executed. */
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@ -691,6 +691,11 @@ public:
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{
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return !initialized_p () || m_quality >= profile_guessed_global0;
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}
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/* Return true if quality of profile is precise. */
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bool precise_p () const
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{
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return m_quality == profile_precise;
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}
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/* When merging basic blocks, the two different profile counts are unified.
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Return true if this can be done without losing info about profile.
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