[pypy-dev] Pypy garbage collection
Maciej Fijalkowski
fijall at gmail.com
Mon Mar 17 14:20:57 CET 2014
are you *sure* it's the walkroots that take that long and not
something else (like gc-minor)? More of those mean that you allocate a
lot more surviving objects. Can you do two things:
a) take a max of gc-minor (and gc-minor-stackwalk), per request
b) take the sum of those
and plot them
On Mon, Mar 17, 2014 at 3:18 PM, Martin Koch <mak at issuu.com> wrote:
> Well, then it works out to around 2.5GHz, which seems reasonable. But it
> doesn't alter the conclusion from the previous email: The slow queries then
> all have a duration around 34*10^9 units, 'normal' queries 1*10^9 units, or
> .4 seconds at this conversion. Also, the log shows that a slow query
> performs many more gc-minor operations than a 'normal' one: 9600
> gc-collect-step/gc-minor/gc-minor-walkroots operations vs 58.
>
> So the question becomes: Why do we get this large spike in
> gc-minor-walkroots, and, in particular, is there any way to avoid it :) ?
>
> Thanks,
> /Martin
>
>
> On Mon, Mar 17, 2014 at 1:53 PM, Maciej Fijalkowski <fijall at gmail.com>
> wrote:
>>
>> 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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