eh, this is not what I need I need a max of TIME it took for a gc-minor and the TOTAL time it took for a gc-minor (per query) (ideally same for gc-walkroots and gc-collect-step) On Mon, Mar 17, 2014 at 4:19 PM, Martin Koch <mak@issuu.com> wrote:
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 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@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 > > 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@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 > >> >> 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@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 > >> >>> > 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 :) > >> >> > >> >> > >> > > > > >