##// END OF EJS Templates
util: lower water mark when removing nodes after cost limit reached...
util: lower water mark when removing nodes after cost limit reached See the inline comment for the reasoning here. This is a pretty common strategy for garbage collectors, other cache-like primtives. The performance impact is substantial: $ hg perflrucachedict --size 4 --gets 1000000 --sets 1000000 --mixed 1000000 --costlimit 100 ! inserts w/ cost limit ! wall 1.659181 comb 1.650000 user 1.650000 sys 0.000000 (best of 7) ! wall 1.722122 comb 1.720000 user 1.720000 sys 0.000000 (best of 6) ! mixed w/ cost limit ! wall 1.139955 comb 1.140000 user 1.140000 sys 0.000000 (best of 9) ! wall 1.182513 comb 1.180000 user 1.180000 sys 0.000000 (best of 9) $ hg perflrucachedict --size 1000 --gets 1000000 --sets 1000000 --mixed 1000000 --costlimit 10000 ! inserts ! wall 0.679546 comb 0.680000 user 0.680000 sys 0.000000 (best of 15) ! sets ! wall 0.825147 comb 0.830000 user 0.830000 sys 0.000000 (best of 13) ! inserts w/ cost limit ! wall 25.105273 comb 25.080000 user 25.080000 sys 0.000000 (best of 3) ! wall 1.724397 comb 1.720000 user 1.720000 sys 0.000000 (best of 6) ! mixed ! wall 0.807096 comb 0.810000 user 0.810000 sys 0.000000 (best of 13) ! mixed w/ cost limit ! wall 12.104470 comb 12.070000 user 12.070000 sys 0.000000 (best of 3) ! wall 1.190563 comb 1.190000 user 1.190000 sys 0.000000 (best of 9) $ hg perflrucachedict --size 1000 --gets 1000000 --sets 1000000 --mixed 1000000 --costlimit 10000 --mixedgetfreq 90 ! inserts ! wall 0.711177 comb 0.710000 user 0.710000 sys 0.000000 (best of 14) ! sets ! wall 0.846992 comb 0.850000 user 0.850000 sys 0.000000 (best of 12) ! inserts w/ cost limit ! wall 25.963028 comb 25.960000 user 25.960000 sys 0.000000 (best of 3) ! wall 2.184311 comb 2.180000 user 2.180000 sys 0.000000 (best of 5) ! mixed ! wall 0.728256 comb 0.730000 user 0.730000 sys 0.000000 (best of 14) ! mixed w/ cost limit ! wall 3.174256 comb 3.170000 user 3.170000 sys 0.000000 (best of 4) ! wall 0.773186 comb 0.770000 user 0.770000 sys 0.000000 (best of 13) $ hg perflrucachedict --size 100000 --gets 1000000 --sets 1000000 --mixed 1000000 --mixedgetfreq 90 --costlimit 5000000 ! gets ! wall 1.191368 comb 1.190000 user 1.190000 sys 0.000000 (best of 9) ! wall 1.195304 comb 1.190000 user 1.190000 sys 0.000000 (best of 9) ! inserts ! wall 0.950995 comb 0.950000 user 0.950000 sys 0.000000 (best of 11) ! inserts w/ cost limit ! wall 1.589732 comb 1.590000 user 1.590000 sys 0.000000 (best of 7) ! sets ! wall 1.094941 comb 1.100000 user 1.090000 sys 0.010000 (best of 9) ! mixed ! wall 0.936420 comb 0.940000 user 0.930000 sys 0.010000 (best of 10) ! mixed w/ cost limit ! wall 0.882780 comb 0.870000 user 0.870000 sys 0.000000 (best of 11) This puts us ~2x slower than caches without cost accounting. And for read-heavy workloads (the prime use cases for caches), performance is nearly identical. In the worst case (pure write workloads with cost accounting enabled), we're looking at ~1.5us per insert on large caches. That seems "fast enough." Differential Revision: https://phab.mercurial-scm.org/D4505

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lsprofcalltree.py
86 lines | 2.7 KiB | text/x-python | PythonLexer
"""
lsprofcalltree.py - lsprof output which is readable by kcachegrind
Authors:
* David Allouche <david <at> allouche.net>
* Jp Calderone & Itamar Shtull-Trauring
* Johan Dahlin
This software may be used and distributed according to the terms
of the GNU General Public License, incorporated herein by reference.
"""
from __future__ import absolute_import, print_function
def label(code):
if isinstance(code, str):
return '~' + code # built-in functions ('~' sorts at the end)
else:
return '%s %s:%d' % (code.co_name,
code.co_filename,
code.co_firstlineno)
class KCacheGrind(object):
def __init__(self, profiler):
self.data = profiler.getstats()
self.out_file = None
def output(self, out_file):
self.out_file = out_file
print('events: Ticks', file=out_file)
self._print_summary()
for entry in self.data:
self._entry(entry)
def _print_summary(self):
max_cost = 0
for entry in self.data:
totaltime = int(entry.totaltime * 1000)
max_cost = max(max_cost, totaltime)
print('summary: %d' % max_cost, file=self.out_file)
def _entry(self, entry):
out_file = self.out_file
code = entry.code
if isinstance(code, str):
print('fi=~', file=out_file)
else:
print('fi=%s' % code.co_filename, file=out_file)
print('fn=%s' % label(code), file=out_file)
inlinetime = int(entry.inlinetime * 1000)
if isinstance(code, str):
print('0 ', inlinetime, file=out_file)
else:
print('%d %d' % (code.co_firstlineno, inlinetime), file=out_file)
# recursive calls are counted in entry.calls
if entry.calls:
calls = entry.calls
else:
calls = []
if isinstance(code, str):
lineno = 0
else:
lineno = code.co_firstlineno
for subentry in calls:
self._subentry(lineno, subentry)
print(file=out_file)
def _subentry(self, lineno, subentry):
out_file = self.out_file
code = subentry.code
print('cfn=%s' % label(code), file=out_file)
if isinstance(code, str):
print('cfi=~', file=out_file)
print('calls=%d 0' % subentry.callcount, file=out_file)
else:
print('cfi=%s' % code.co_filename, file=out_file)
print('calls=%d %d' % (
subentry.callcount, code.co_firstlineno), file=out_file)
totaltime = int(subentry.totaltime * 1000)
print('%d %d' % (lineno, totaltime), file=out_file)