[pypy-dev] Pypy garbage collection
Martin Koch
mak at issuu.com
Mon Mar 17 15:19:28 CET 2014
Here are the collated results of running each query. For each run, I count
how many of each of the pypy debug lines i get. I.e. there were 668 runs
that printed 58 loglines that contain "{gc-minor" which was eventually
followed by "gc-minor}". I have also counted if the query was slow;
interestingly, not all the queries with many gc-minors were slow (but all
slow queries had a gc-minor).
Please let me know if this is unclear :)
668 gc-minor:58 gc-minor-walkroots:58
10 gc-minor:58 gc-minor-walkroots:58 gc-collect-step:5
140 gc-minor:59 gc-minor-walkroots:59
1 gc-minor:8441 gc-minor-walkroots:8441 gc-collect-step:8403
1 gc-minor:9300 gc-minor-walkroots:9300 gc-collect-step:9249
9 gc-minor:9643 *slow*:1 gc-minor-walkroots:9643 gc-collect-step:9589
1 gc-minor:9644 *slow*:1 gc-minor-walkroots:9644 gc-collect-step:9590
10 gc-minor:9647 *slow*:1 gc-minor-walkroots:9647 gc-collect-step:9609
1 gc-minor:9663 gc-minor-walkroots:9663 gc-collect-step:9614
1 jit-backend-dump:5 gc-minor:58 gc-minor-walkroots:58
1 jit-log-compiling-loop:1 gc-collect-step:8991 jit-backend-dump:78
jit-backend:3 jit-log-noopt-loop:6 jit-log-virtualstate:3 gc-minor:9030
jit-tracing:3 gc-minor-walkroots:9030 jit-optimize:6
jit-log-short-preamble:2 jit-backend-addr:3 jit-log-opt-loop:1
jit-mem-looptoken-alloc:3 jit-abort:3 jit-log-rewritten-bridge:2
jit-log-rewritten-loop:1 jit-log-opt-bridge:2 jit-log-compiling-bridge:2
jit-resume:84
1 jit-log-compiling-loop:1 jit-backend-dump:13 jit-backend:1
jit-log-noopt-loop:2 gc-minor:60 jit-tracing:1 gc-minor-walkroots:60
jit-optimize:2 jit-log-short-preamble:1 jit-backend-addr:1
jit-log-opt-loop:1 jit-mem-looptoken-alloc:1 jit-log-rewritten-loop:1
jit-resume:14
1 jit-log-compiling-loop:1 jit-backend-dump:73 jit-backend:3
jit-log-noopt-loop:6 jit-log-virtualstate:3 gc-minor:60 jit-tracing:3
gc-minor-walkroots:60 jit-optimize:6 jit-log-short-preamble:2
jit-backend-addr:3 jit-log-opt-loop:1 jit-mem-looptoken-alloc:3 jit-abort:3
jit-log-rewritten-bridge:2 jit-log-rewritten-loop:1 jit-log-opt-bridge:2
jit-log-compiling-bridge:2 jit-resume:84
2 jit-log-compiling-loop:1 jit-backend-dump:78 jit-backend:3
jit-log-noopt-loop:6 jit-log-virtualstate:3 gc-minor:61 jit-tracing:3
gc-minor-walkroots:61 jit-optimize:6 jit-log-short-preamble:2
jit-backend-addr:3 jit-log-opt-loop:1 jit-mem-looptoken-alloc:3 jit-abort:3
jit-log-rewritten-bridge:2 jit-log-rewritten-loop:1 jit-log-opt-bridge:2
jit-log-compiling-bridge:2 jit-resume:84
1 jit-log-short-preamble:2 jit-log-compiling-loop:2
jit-backend-dump:92 jit-log-noopt-loop:7 jit-log-virtualstate:3 gc-minor:61
jit-tracing:4 gc-minor-walkroots:61 jit-optimize:7 jit-backend:4
jit-backend-addr:4 jit-log-opt-loop:2 jit-mem-looptoken-alloc:4 jit-abort:3
jit-log-rewritten-bridge:2 jit-log-rewritten-loop:2 jit-log-opt-bridge:2
jit-log-compiling-bridge:2 jit-resume:104
Thanks,
/Martin
On Mon, Mar 17, 2014 at 2:23 PM, Maciej Fijalkowski <fijall at gmail.com>wrote:
> On Mon, Mar 17, 2014 at 3:20 PM, Maciej Fijalkowski <fijall at gmail.com>
> wrote:
> > 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
>
> ^^^ or just paste the results actually
>
> >
> > 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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