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@gmail.com>wrote:
On Mon, Mar 17, 2014 at 3:20 PM, Maciej Fijalkowski <fijall@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@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
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@gmail.com> wrote:
I think it's the cycles of your CPU
On Mon, Mar 17, 2014 at 2:48 PM, Martin Koch <mak@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@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@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
> 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@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@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@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
>> >> 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
>> >> 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@gmail.com> >> >> wrote: >> >>> >> >>> On Thu, Mar 13, 2014 at 1:45 PM, Martin Koch <mak@issuu.com> >> >>> wrote: >> >>> > Hi Armin, Maciej >> >>> > >> >>> > Thanks for responding. >> >>> > >> >>> > I'm in the process of trying to determine what (if any) of
then the list, the 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 :) >> >> >> >> >> > > >