PMJI, but I wonder if some of these objects could come from trivial re-instatiations instead of re-use of mutable objects, e.g., fishing out one attribute to use together with a new value as init values for an (unnecessarily) new obj. obj = ObjClass(obj.someattr, chgval) where obj.chg = chgval would have done the job without creating garbage. I suspect this pattern can happen more subtly than above, especially if __new__ is defined to do something tricky with old instances. Also, creating a new object can be a tempting way to feel sure about its complete state, without having to write a custom (re)init method. On 03/18/2014 10:37 AM Martin Koch wrote:
Thanks, Carl.
This bit of code certainly exhibits the surprising property that some runs unpredictably stall for a very long time. Further, it seems that this stall time can be made arbitrarily large by increasing the number of nodes generated (== more data in the old generation == more stuff to traverse if lots of garbage is generated and survives the young generation?). As a user of an incremental garbage collector, I would expect that there are pauses due to GC, but that these are predictable and small.
I tried running
PYPY_GC_NURSERY=2000M pypy ./mem.py 10000000
but that seemed to have no effect.
I'm looking forward to the results of the Software Transactional Memory, btw :)
/Martin
On Tue, Mar 18, 2014 at 9:47 AM, Carl Friedrich Bolz<cfbolz@gmx.de> wrote:
On 17/03/14 20:04, Martin Koch wrote:
Well, it would appear that we have the problem because we're generating a lot of garbage in the young generation, just like we're doing in the example we've been studying here.
No, I think it's because your generating a lot of garbage in the *old* generation. Meaning objects which survive one minor collection but then die.
I'm unsure how we can avoid that in our real implementation. Can we force gc of the young generation? Either by gc.collect() or implcitly somehow (does the gc e.g. kick in across function calls?).
That would make matters worse, because increasing the frequency of minor collects means *more* objects get moved to the old generation (where they cause problems). So indeed, maybe in your case making the new generation bigger might help. This can be done using PYPY_GC_NURSERY, I think (nursery is the space reserved for young objects). The risk is that minor collections become unreasonably slow.
Anyway, if the example code you gave us also shows the problem I think we should eventually look into it. It's not really fair to say "but you're allocating too much!" to explain why the GC takes a lot of time.
Cheers,
Carl Friedrich _______________________________________________ pypy-dev mailing list pypy-dev@python.org https://mail.python.org/mailman/listinfo/pypy-dev
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