Ah. I had misunderstood. I'll get back to you on that :) thanks /Martin
On 17/03/2014, at 15.21, Maciej Fijalkowski <fijall@gmail.com> wrote:
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 :) >>>>>> >>>>>> >>>>> >>> >>> > >