mirror of
https://github.com/panda3d/panda3d.git
synced 2025-10-04 02:42:49 -04:00
accurate profile duration tracking, output filtering (no 0.000 second spikes), preliminary profile result aggregation
This commit is contained in:
Darren Ranalli
parent
ef771a796b
commit
61d0f91617
@ -26,12 +26,9 @@ class ProfileSession:
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def __init__(self, func, name):
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self._func = func
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self._name = name
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self._filename = 'profileData-%s-%s' % (self._name, id(self))
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self._profileSucceeded = False
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self._wallClockDur = None
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self._ramFile = None
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self._filenameBase = 'profileData-%s-%s' % (self._name, id(self))
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self._refCount = 0
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self._resultCache = {}
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self._reset()
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self.acquire()
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def acquire(self):
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@ -44,28 +41,51 @@ class ProfileSession:
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def _destroy(self):
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del self._func
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del self._name
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del self._filename
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del self._wallClockDur
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del self._ramFile
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del self._filenameBase
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del self._filenameCounter
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del self._filenames
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del self._duration
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del self._filename2ramFile
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del self._resultCache
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del self._successfulProfiles
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def run(self):
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def _reset(self):
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self._filenameCounter = 0
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self._filenames = []
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# index of next file to be added to stats object
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self._statFileCounter = 0
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self._successfulProfiles = 0
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self._duration = None
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self._filename2ramFile = {}
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self._stats = None
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self._resultCache = {}
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def _getNextFilename(self):
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filename = '%s-%s' % (self._filenameBase, self._filenameCounter)
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self._filenameCounter += 1
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return filename
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def run(self, aggregate=True):
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# make sure this instance doesn't get destroyed inside self._func
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self.acquire()
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self._profileSucceeded = False
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if not aggregate:
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self._reset()
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# if we're already profiling, just run the func and don't profile
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if 'globalProfileFunc' in __builtin__.__dict__:
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result = self._func()
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self._wallClockDur = 0.
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if self._duration is None:
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self._duration = 0.
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else:
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# put the function in the global namespace so that profile can find it
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__builtin__.globalProfileFunc = self._func
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__builtin__.globalProfileResult = [None]
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# set up the RAM file
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_installProfileCustomFuncs(self._filename)
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self._filenames.append(self._getNextFilename())
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filename = self._filenames[-1]
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_installProfileCustomFuncs(filename)
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# do the profiling
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Profile = profile.Profile
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@ -76,55 +96,87 @@ class ProfileSession:
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# this is based on profile.run, the code is replicated here to allow us to
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# eliminate a memory leak
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prof = Profile()
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# try to get wall-clock duration that is as accurate as possible
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startT = self.TrueClock.getShortTime()
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try:
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prof = prof.run(statement)
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except SystemExit:
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pass
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# try to get wall-clock duration that is as accurate as possible
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endT = self.TrueClock.getShortTime()
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# this has to be run immediately after profiling for the timings to be accurate
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# tell the Profile object to generate output to the RAM file
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prof.dump_stats(self._filename)
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dur = endT - startT
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prof.dump_stats(filename)
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# eliminate the memory leak
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del prof.dispatcher
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# and store the results
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self._wallClockDur = dur
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# store the RAM file for later
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self._ramFile = _getProfileResultFileInfo(self._filename)
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profData = _getProfileResultFileInfo(filename)
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self._filename2ramFile[filename] = profData
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# calculate the duration (this is dependent on the internal Python profile data format.
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# see profile.py and pstats.py, this was copied from pstats.Stats.strip_dirs)
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maxTime = 0.
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for cc, nc, tt, ct, callers in profData[1].itervalues():
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if ct > maxTime:
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maxTime = ct
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self._duration = maxTime
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# clean up the RAM file support
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_removeProfileCustomFuncs(self._filename)
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_removeProfileCustomFuncs(filename)
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# clean up the globals
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result = globalProfileResult[0]
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del __builtin__.__dict__['globalProfileFunc']
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del __builtin__.__dict__['globalProfileResult']
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self._profileSucceeded = True
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self._successfulProfiles += 1
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self.release()
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return result
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def getWallClockDuration(self):
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# this might not be accurate, it may include time taken up by other processes
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return self._wallClockDur
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def getDuration(self):
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return self._duration
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def profileSucceeded(self):
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return self._profileSucceeded
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return self._successfulProfiles > 0
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def _restoreRamFile(self, filename):
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# set up the RAM file
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_installProfileCustomFuncs(filename)
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# install the stored RAM file from self.run()
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_setProfileResultsFileInfo(filename, self._filename2ramFile[filename])
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def _discardRamFile(self, filename):
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# take down the RAM file
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_removeProfileCustomFuncs(filename)
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# and discard it
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del self._filename2ramFile[filename]
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def getResults(self,
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lines=80,
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sorts=['cumulative', 'time', 'calls'],
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callInfo=False):
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if not self._profileSucceeded:
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if not self.profileSucceeded():
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output = '%s: profiler already running, could not profile' % self._name
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else:
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# make sure our stats object exists and is up-to-date
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statsChanged = (self._statFileCounter < len(self._filenames))
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if self._stats is None:
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for filename in self._filenames:
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self._restoreRamFile(filename)
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self._stats = pstats.Stats(*self._filenames)
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self._statFileCounter = len(self._filenames)
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for filename in self._filenames:
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self._discardRamFile(filename)
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else:
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while self._statFileCounter < len(self._filenames):
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filename = self._filenames[self._statFileCounter]
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self._restoreRamFile(filename)
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self._stats.add(filename)
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self._discardRamFile(filename)
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if statsChanged:
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self._stats.strip_dirs()
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# throw out any cached result strings
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self._resultCache = {}
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# make sure the arguments will hash efficiently if callers provide different types
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lines = int(lines)
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sorts = list(sorts)
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@ -134,19 +186,13 @@ class ProfileSession:
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# we've already created this output string, get it from the cache
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output = self._resultCache[k]
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else:
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# set up the RAM file
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_installProfileCustomFuncs(self._filename)
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# install the stored RAM file from self.run()
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_setProfileResultsFileInfo(self._filename, self._ramFile)
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# now extract human-readable output from the RAM file
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# now get human-readable output from the profile stats
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# capture print output to a string
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sc = StdoutCapture()
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# print the info to stdout
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s = pstats.Stats(self._filename)
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s.strip_dirs()
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s = self._stats
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for sort in sorts:
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s.sort_stats(sort)
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s.print_stats(lines)
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@ -160,9 +206,6 @@ class ProfileSession:
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# restore stdout to what it was before
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sc.destroy()
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# clean up the RAM file support
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_removeProfileCustomFuncs(self._filename)
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# cache this result
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self._resultCache[k] = output
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@ -5,11 +5,18 @@ from direct.showbase.PythonUtil import Averager
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class TaskTracker:
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# call it TaskProfiler to avoid confusion for the user
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notify = directNotify.newCategory("TaskProfiler")
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MinSamples = None
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SpikeThreshold = None
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def __init__(self, namePrefix):
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self._namePrefix = namePrefix
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self._durationAverager = Averager('%s-durationAverager' % namePrefix)
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self._avgSession = None
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self._maxNonSpikeSession = None
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if TaskTracker.MinSamples is None:
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# number of samples required before spikes start getting identified
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TaskTracker.MinSamples = config.GetInt('profile-task-spike-min-samples', 30)
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# defines spike as longer than this multiple of avg task duration
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TaskTracker.SpikeThreshold = config.GetFloat('profile-task-spike-threshold', 10.)
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def destroy(self):
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self.flush()
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del self._namePrefix
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@ -24,14 +31,43 @@ class TaskTracker:
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self._maxNonSpikeSession = None
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def getNamePrefix(self, namePrefix):
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return self._namePrefix
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def addProfileSession(self, session, isSpike):
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duration = session.getWallClockDuration()
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def _checkSpike(self, session):
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duration = session.getDuration()
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isSpike = False
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# was it long enough to show up on a printout?
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if duration >= .001:
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# do we have enough samples?
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if self.getNumDurationSamples() > self.MinSamples:
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# was this a spike?
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if duration > (self.getAvgDuration() * self.SpikeThreshold):
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isSpike = True
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avgSession = self.getAvgSession()
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maxNSSession = self.getMaxNonSpikeSession()
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self.notify.info('task CPU spike profile (%s):\n'
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'== AVERAGE (%s seconds)\n%s\n'
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'== LONGEST NON-SPIKE (%s seconds)\n%s\n'
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'== SPIKE (%s seconds)\n%s' % (
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self._namePrefix,
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avgSession.getDuration(), avgSession.getResults(),
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maxNSSession.getDuration(), maxNSSession.getResults(),
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duration, session.getResults()))
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return isSpike
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def addProfileSession(self, session):
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isSpike = self._checkSpike(session)
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duration = session.getDuration()
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self._durationAverager.addValue(duration)
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if self.getNumDurationSamples() == self.MinSamples:
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# if we just collected enough samples to check for spikes, see if
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# our max 'non-spike' session collected so far counts as a spike
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# and if so, log it
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if self._checkSpike(self._maxNonSpikeSession):
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self._maxNonSpikeSession = self._avgSession
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storeAvg = True
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storeMaxNS = True
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if self._avgSession is not None:
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avgDur = self.getAvgDuration()
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if abs(self._avgSession.getWallClockDuration() - avgDur) < abs(duration - avgDur):
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if abs(self._avgSession.getDuration() - avgDur) < abs(duration - avgDur):
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# current avg data is more average than this new sample, keep the data we've
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# already got stored
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storeAvg = False
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@ -39,7 +75,7 @@ class TaskTracker:
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storeMaxNS = False
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else:
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if (self._maxNonSpikeSession is not None and
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self._maxNonSpikeSession.getWallClockDuration() > duration):
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self._maxNonSpikeSession.getDuration() > duration):
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storeMaxNS = False
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if storeAvg:
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if self._avgSession:
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@ -64,12 +100,12 @@ class TaskTracker:
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def log(self):
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if self._avgSession:
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self.notify.info('task CPU profile (%s):\n'
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'== AVERAGE (%s wall-clock seconds)\n%s\n'
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'== LONGEST NON-SPIKE (%s wall-clock seconds)\n%s' % (
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'== AVERAGE (%s seconds)\n%s\n'
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'== LONGEST NON-SPIKE (%s seconds)\n%s' % (
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self._namePrefix,
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self._avgSession.getWallClockDuration(),
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self._avgSession.getDuration(),
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self._avgSession.getResults(),
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self._maxNonSpikeSession.getWallClockDuration(),
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self._maxNonSpikeSession.getDuration(),
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self._maxNonSpikeSession.getResults(),
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))
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else:
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@ -85,10 +121,6 @@ class TaskProfiler:
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# table of task name pattern to TaskTracker
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self._namePrefix2tracker = {}
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self._task = None
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# number of samples required before spikes start getting identified
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self._minSamples = config.GetInt('profile-task-spike-min-samples', 30)
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# defines spike as longer than this multiple of avg task duration
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self._spikeThreshold = config.GetFloat('profile-task-spike-threshold', 10.)
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def destroy(self):
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if taskMgr.getProfileTasks():
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@ -131,30 +163,11 @@ class TaskProfiler:
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session = taskMgr._getLastProfileSession()
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# if we couldn't profile, throw this result out
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if session.profileSucceeded():
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sessionDur = session.getWallClockDuration()
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namePrefix = self._task.getNamePrefix()
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if namePrefix not in self._namePrefix2tracker:
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self._namePrefix2tracker[namePrefix] = TaskTracker(namePrefix)
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tracker = self._namePrefix2tracker[namePrefix]
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isSpike = False
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# do we have enough samples?
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if tracker.getNumDurationSamples() > self._minSamples:
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# was this a spike?
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if sessionDur > (tracker.getAvgDuration() * self._spikeThreshold):
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print 'sessionDur=%s' % sessionDur
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print 'avgDur=%s' % tracker.getAvgDuration()
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isSpike = True
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avgSession = tracker.getAvgSession()
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maxNSSession = tracker.getMaxNonSpikeSession()
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self.notify.info('task CPU spike profile (%s):\n'
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'== AVERAGE (%s wall-clock seconds)\n%s\n'
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'== LONGEST NON-SPIKE (%s wall-clock seconds)\n%s\n'
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'== SPIKE (%s wall-clock seconds)\n%s' % (
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namePrefix,
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avgSession.getWallClockDuration(), avgSession.getResults(),
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maxNSSession.getWallClockDuration(), maxNSSession.getResults(),
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sessionDur, session.getResults()))
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tracker.addProfileSession(session, isSpike)
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tracker.addProfileSession(session)
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# set up the next task
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self._task = taskMgr._getRandomTask()
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