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
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