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@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 :) >> >> >