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 <[email protected]> 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 <[email protected]> > wrote: >> >> I think it's the cycles of your CPU >> >> On Mon, Mar 17, 2014 at 2:48 PM, Martin Koch <[email protected]> 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 <[email protected]> >> > wrote: >> >> >> >> The number of lines is nonsense. This is a timestamp in hex. >> >> >> >> On Mon, Mar 17, 2014 at 12:46 PM, Martin Koch <[email protected]> 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 >> >> > <[email protected]> >> >> > 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 <[email protected]> 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 <[email protected]> >> >> >> > 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 >> >> >> >> <[email protected]> >> >> >> >> wrote: >> >> >> >>> >> >> >> >>> On Thu, Mar 13, 2014 at 1:45 PM, Martin Koch <[email protected]> >> >> >> >>> 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 :) >> >> >> >> >> >> >> >> >> >> >> > >> >> > >> >> > >> > >> > > > _______________________________________________ pypy-dev mailing list [email protected] https://mail.python.org/mailman/listinfo/pypy-dev
