First patch from the series simplifies how we dump and post-process statistics.
I switched to use ';' separated values instead of a complex human language.
Changes
to analyze_brprob.py add possibility to filter out dominant edges. Having that
one
can see how predictor values change if a dominant edge is ignored.
Martin
>From ef3c162961f3599ee65e87ecde5138d9f37f0221 Mon Sep 17 00:00:00 2001
From: marxin <mli...@suse.cz>
Date: Thu, 21 Dec 2017 17:19:13 +0100
Subject: [PATCH 1/5] Fix usage of analyze_brprob.py script.
contrib/ChangeLog:
2017-12-21 Martin Liska <mli...@suse.cz>
* analyze_brprob.py: Support new format that can be easily
parsed. Add new column to report.
gcc/ChangeLog:
2017-12-21 Martin Liska <mli...@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.
---
contrib/analyze_brprob.py | 103 ++++++++++++++++++++++++++++++++--------------
gcc/predict.c | 13 ++++++
gcc/profile-count.h | 5 +++
3 files changed, 90 insertions(+), 31 deletions(-)
diff --git a/contrib/analyze_brprob.py b/contrib/analyze_brprob.py
index e03d1da1cde..de5f474d629 100755
--- a/contrib/analyze_brprob.py
+++ b/contrib/analyze_brprob.py
@@ -71,6 +71,7 @@ from math import *
counter_aggregates = set(['combined', 'first match', 'DS theory',
'no prediction'])
+hot_threshold = 10
def percentage(a, b):
return 100.0 * a / b
@@ -131,47 +132,87 @@ class PredictDefFile:
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
+ 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 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)), end = '')
+ 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:
@@ -185,33 +226,29 @@ class Profile:
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, 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':
@@ -221,10 +258,10 @@ class Profile:
elif sorting == 'name':
sorter = lambda x: x.name.lower()
- print('%-40s %8s %6s %12s %18s %14s %8s %6s %12s %6s' %
+ print('%-40s %8s %6s %12s %18s %14s %8s %6s %12s %6s %s' %
('HEURISTICS', 'BRANCHES', '(REL)',
'BR. HITRATE', 'HITRATE', 'COVERAGE', 'COVERAGE', '(REL)',
- 'predict.def', '(REL)'))
+ 'predict.def', '(REL)', 'HOT branches (>%d%%)' % hot_threshold))
for h in sorted(heuristics, key = sorter):
h.print(branches_max, count_max, predict_def)
@@ -266,19 +303,23 @@ parser.add_argument('-s', '--sorting', dest = 'sorting',
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(args.dump_file)
-r = re.compile(' (.*) heuristics( of edge [0-9]*->[0-9]*)?( \\(.*\\))?: (.*)%.*exec ([0-9]*) hit ([0-9]*)')
loop_niter_str = ';; profile-based iteration count: '
+
for l in open(args.dump_file):
- 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))
+ 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):
diff --git a/gcc/predict.c b/gcc/predict.c
index 9cea90d1063..3ac18a2c5f2 100644
--- a/gcc/predict.c
+++ b/gcc/predict.c
@@ -747,6 +747,19 @@ dump_prediction (FILE *file, enum br_predictor predictor, int probability,
}
fprintf (file, "\n");
+
+ /* Print output that be easily read by analyze_brprob.py script. We are
+ interested only in counts that are read from GCDA files. */
+ if (dump_file && (dump_flags & TDF_DETAILS)
+ && bb->count.precise_p ()
+ && reason == REASON_NONE)
+ {
+ gcc_assert (e->count ().precise_p ());
+ fprintf (file, ";;heuristics;%s;%" PRId64 ";%" PRId64 ";%.1f;\n",
+ predictor_info[predictor].name,
+ bb->count.to_gcov_type (), e->count ().to_gcov_type (),
+ probability * 100.0 / REG_BR_PROB_BASE);
+ }
}
/* Return true if STMT is known to be unlikely executed. */
diff --git a/gcc/profile-count.h b/gcc/profile-count.h
index 3c5f720ee81..3a37b1293e5 100644
--- a/gcc/profile-count.h
+++ b/gcc/profile-count.h
@@ -689,6 +689,11 @@ public:
{
return !initialized_p () || m_quality >= profile_guessed_global0;
}
+ /* Return true if quality of profile is precise. */
+ bool precise_p () const
+ {
+ return m_quality == profile_precise;
+ }
/* When merging basic blocks, the two different profile counts are unified.
Return true if this can be done without losing info about profile.
--
2.14.3
