[pypy-dev] Pypy garbage collection
Maciej Fijalkowski
fijall at gmail.com
Mon Mar 17 13:53:20 CET 2014
I think it's the cycles of your CPU
On Mon, Mar 17, 2014 at 2:48 PM, Martin Koch <mak at issuu.com> wrote:
> What is the unit? Perhaps I'm being thick here, but I can't correlate it
> with seconds (which the program does print out). Slow runs are around 13
> seconds, but are around 34*10^9(dec), 0x800000000 timestamp units (e.g. from
> 0x2b994c9d31889c to 0x2b9944ab8c4f49).
>
>
>
> On Mon, Mar 17, 2014 at 12:09 PM, Maciej Fijalkowski <fijall at gmail.com>
> wrote:
>>
>> The number of lines is nonsense. This is a timestamp in hex.
>>
>> On Mon, Mar 17, 2014 at 12:46 PM, Martin Koch <mak at issuu.com> wrote:
>> > Based On Maciej's suggestion, I tried the following
>> >
>> > PYPYLOG=- pypy mem.py 10000000 > out
>> >
>> > This generates a logfile which looks something like this
>> >
>> > start-->
>> > [2b99f1981b527e] {gc-minor
>> > [2b99f1981ba680] {gc-minor-walkroots
>> > [2b99f1981c2e02] gc-minor-walkroots}
>> > [2b99f19890d750] gc-minor}
>> > [snip]
>> > ...
>> > <--stop
>> >
>> >
>> > It turns out that the culprit is a lot of MINOR collections.
>> >
>> > I base this on the following observations:
>> >
>> > I can't understand the format of the timestamp on each logline (the
>> > "[2b99f1981b527e]"). From what I can see in the code, this should be
>> > output
>> > from time.clock(), but that doesn't return a number like that when I run
>> > pypy interactively
>> > Instead, I count the number of debug lines between start--> and the
>> > corresponding <--stop.
>> > Most runs have a few hundred lines of output between start/stop
>> > All slow runs have very close to 57800 lines out output between
>> > start/stop
>> > One such sample does 9609 gc-collect-step operations, 9647 gc-minor
>> > operations, and 9647 gc-minor-walkroots operations.
>> >
>> >
>> > Thanks,
>> > /Martin
>> >
>> >
>> > On Mon, Mar 17, 2014 at 8:21 AM, Maciej Fijalkowski <fijall at gmail.com>
>> > wrote:
>> >>
>> >> there is an environment variable PYPYLOG=gc:- (where - is stdout)
>> >> which will do that for you btw.
>> >>
>> >> maybe you can find out what's that using profiling or valgrind?
>> >>
>> >> On Sun, Mar 16, 2014 at 11:34 PM, Martin Koch <mak at issuu.com> wrote:
>> >> > I have tried getting the pypy source and building my own version of
>> >> > pypy. I
>> >> > have modified
>> >> > rpython/memory/gc/incminimark.py:major_collection_step()
>> >> > to
>> >> > print out when it starts and when it stops. Apparently, the slow
>> >> > queries
>> >> > do
>> >> > NOT occur during major_collection_step; at least, I have not observed
>> >> > major
>> >> > step output during a query execution. So, apparently, something else
>> >> > is
>> >> > blocking. This could be another aspect of the GC, but it could also
>> >> > be
>> >> > anything else.
>> >> >
>> >> > Just to be sure, I have tried running the same application in python
>> >> > with
>> >> > garbage collection disabled. I don't see the problem there, so it is
>> >> > somehow
>> >> > related to either GC or the runtime somehow.
>> >> >
>> >> > Cheers,
>> >> > /Martin
>> >> >
>> >> >
>> >> > On Fri, Mar 14, 2014 at 4:19 PM, Martin Koch <mak at issuu.com> wrote:
>> >> >>
>> >> >> We have hacked up a small sample that seems to exhibit the same
>> >> >> issue.
>> >> >>
>> >> >> We basically generate a linked list of objects. To increase
>> >> >> connectedness,
>> >> >> elements in the list hold references (dummy_links) to 10 randomly
>> >> >> chosen
>> >> >> previous elements in the list.
>> >> >>
>> >> >> We then time a function that traverses 50000 elements from the list
>> >> >> from a
>> >> >> random start point. If the traversal reaches the end of the list, we
>> >> >> instead
>> >> >> traverse one of the dummy links. Thus, exactly 50K elements are
>> >> >> traversed
>> >> >> every time. To generate some garbage, we build a list holding the
>> >> >> traversed
>> >> >> elements and a dummy list of characters.
>> >> >>
>> >> >> Timings for the last 100 runs are stored in a circular buffer. If
>> >> >> the
>> >> >> elapsed time for the last run is more than twice the average time,
>> >> >> we
>> >> >> print
>> >> >> out a line with the elapsed time, the threshold, and the 90% runtime
>> >> >> (we
>> >> >> would like to see that the mean runtime does not increase with the
>> >> >> number of
>> >> >> elements in the list, but that the max time does increase (linearly
>> >> >> with the
>> >> >> number of object, i guess); traversing 50K elements should be
>> >> >> independent of
>> >> >> the memory size).
>> >> >>
>> >> >> We have tried monitoring memory consumption by external inspection,
>> >> >> but
>> >> >> cannot consistently verify that memory is deallocated at the same
>> >> >> time
>> >> >> that
>> >> >> we see slow requests. Perhaps the pypy runtime doesn't always return
>> >> >> freed
>> >> >> pages back to the OS?
>> >> >>
>> >> >> Using top, we observe that 10M elements allocates around 17GB after
>> >> >> building, 20M elements 26GB, 30M elements 28GB (and grows to 35GB
>> >> >> shortly
>> >> >> after building).
>> >> >>
>> >> >> Here is output from a few runs with different number of elements:
>> >> >>
>> >> >>
>> >> >> pypy mem.py 10000000
>> >> >> start build
>> >> >> end build 84.142424
>> >> >> that took a long time elapsed: 13.230586 slow_threshold: 1.495401
>> >> >> 90th_quantile_runtime: 0.421558
>> >> >> that took a long time elapsed: 13.016531 slow_threshold: 1.488160
>> >> >> 90th_quantile_runtime: 0.423441
>> >> >> that took a long time elapsed: 13.032537 slow_threshold: 1.474563
>> >> >> 90th_quantile_runtime: 0.419817
>> >> >>
>> >> >> pypy mem.py 20000000
>> >> >> start build
>> >> >> end build 180.823105
>> >> >> that took a long time elapsed: 27.346064 slow_threshold: 2.295146
>> >> >> 90th_quantile_runtime: 0.434726
>> >> >> that took a long time elapsed: 26.028852 slow_threshold: 2.283927
>> >> >> 90th_quantile_runtime: 0.374190
>> >> >> that took a long time elapsed: 25.432279 slow_threshold: 2.279631
>> >> >> 90th_quantile_runtime: 0.371502
>> >> >>
>> >> >> pypy mem.py 30000000
>> >> >> start build
>> >> >> end build 276.217811
>> >> >> that took a long time elapsed: 40.993855 slow_threshold: 3.188464
>> >> >> 90th_quantile_runtime: 0.459891
>> >> >> that took a long time elapsed: 41.693553 slow_threshold: 3.183003
>> >> >> 90th_quantile_runtime: 0.393654
>> >> >> that took a long time elapsed: 39.679769 slow_threshold: 3.190782
>> >> >> 90th_quantile_runtime: 0.393677
>> >> >> that took a long time elapsed: 43.573411 slow_threshold: 3.239637
>> >> >> 90th_quantile_runtime: 0.393654
>> >> >>
>> >> >> Code below
>> >> >> --------------------------------------------------------------
>> >> >> import time
>> >> >> from random import randint, choice
>> >> >> import sys
>> >> >>
>> >> >>
>> >> >> allElems = {}
>> >> >>
>> >> >> class Node:
>> >> >> def __init__(self, v_):
>> >> >> self.v = v_
>> >> >> self.next = None
>> >> >> self.dummy_data = [randint(0,100)
>> >> >> for _ in xrange(randint(50,100))]
>> >> >> allElems[self.v] = self
>> >> >> if self.v > 0:
>> >> >> self.dummy_links = [allElems[randint(0, self.v-1)] for _
>> >> >> in
>> >> >> xrange(10)]
>> >> >> else:
>> >> >> self.dummy_links = [self]
>> >> >>
>> >> >> def set_next(self, l):
>> >> >> self.next = l
>> >> >>
>> >> >>
>> >> >> def follow(node):
>> >> >> acc = []
>> >> >> count = 0
>> >> >> cur = node
>> >> >> assert node.v is not None
>> >> >> assert cur is not None
>> >> >> while count < 50000:
>> >> >> # return a value; generate some garbage
>> >> >> acc.append((cur.v, [choice("abcdefghijklmnopqrstuvwxyz") for
>> >> >> x
>> >> >> in
>> >> >> xrange(100)]))
>> >> >>
>> >> >> # if we have reached the end, chose a random link
>> >> >> cur = choice(cur.dummy_links) if cur.next is None else
>> >> >> cur.next
>> >> >> count += 1
>> >> >>
>> >> >> return acc
>> >> >>
>> >> >>
>> >> >> def build(num_elems):
>> >> >> start = time.time()
>> >> >> print "start build"
>> >> >> root = Node(0)
>> >> >> cur = root
>> >> >> for x in xrange(1, num_elems):
>> >> >> e = Node(x)
>> >> >> cur.next = e
>> >> >> cur = e
>> >> >> print "end build %f" % (time.time() - start)
>> >> >> return root
>> >> >>
>> >> >>
>> >> >> num_timings = 100
>> >> >> if __name__ == "__main__":
>> >> >> num_elems = int(sys.argv[1])
>> >> >> build(num_elems)
>> >> >> total = 0
>> >> >> timings = [0.0] * num_timings # run times for the last
>> >> >> num_timings
>> >> >> runs
>> >> >> i = 0
>> >> >> beginning = time.time()
>> >> >> while time.time() - beginning < 600:
>> >> >> start = time.time()
>> >> >> elem = allElems[randint(0, num_elems - 1)]
>> >> >> assert(elem is not None)
>> >> >>
>> >> >> lst = follow(elem)
>> >> >>
>> >> >> total += choice(lst)[0] # use the return value for something
>> >> >>
>> >> >> end = time.time()
>> >> >>
>> >> >> elapsed = end-start
>> >> >> timings[i % num_timings] = elapsed
>> >> >> if (i > num_timings):
>> >> >> slow_time = 2 * sum(timings)/num_timings # slow defined
>> >> >> as
>> >> >> >
>> >> >> 2*avg run time
>> >> >> if (elapsed > slow_time):
>> >> >> print "that took a long time elapsed: %f
>> >> >> slow_threshold:
>> >> >> %f 90th_quantile_runtime: %f" % \
>> >> >> (elapsed, slow_time,
>> >> >> sorted(timings)[int(num_timings*.9)])
>> >> >> i += 1
>> >> >> print total
>> >> >>
>> >> >>
>> >> >>
>> >> >>
>> >> >>
>> >> >> On Thu, Mar 13, 2014 at 7:45 PM, Maciej Fijalkowski
>> >> >> <fijall at gmail.com>
>> >> >> wrote:
>> >> >>>
>> >> >>> On Thu, Mar 13, 2014 at 1:45 PM, Martin Koch <mak at issuu.com> wrote:
>> >> >>> > Hi Armin, Maciej
>> >> >>> >
>> >> >>> > Thanks for responding.
>> >> >>> >
>> >> >>> > I'm in the process of trying to determine what (if any) of the
>> >> >>> > code
>> >> >>> > I'm
>> >> >>> > in a
>> >> >>> > position to share, and I'll get back to you.
>> >> >>> >
>> >> >>> > Allowing hinting to the GC would be good. Even better would be a
>> >> >>> > means
>> >> >>> > to
>> >> >>> > allow me to (transparently) allocate objects in unmanaged memory,
>> >> >>> > but I
>> >> >>> > would expect that to be a tall order :)
>> >> >>> >
>> >> >>> > Thanks,
>> >> >>> > /Martin
>> >> >>>
>> >> >>> Hi Martin.
>> >> >>>
>> >> >>> Note that in case you want us to do the work of isolating the
>> >> >>> problem,
>> >> >>> we do offer paid support to do that (then we can sign NDAs and
>> >> >>> stuff).
>> >> >>> Otherwise we would be more than happy to fix bugs once you isolate
>> >> >>> a
>> >> >>> part you can share freely :)
>> >> >>
>> >> >>
>> >> >
>> >
>> >
>
>
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