@Nataniel this is what I am suggesting as well. No cacheing - just storing the `fn` on each worker, rather than pickling it for each item in our iterable.
As long as we store the `fn` post-fork on the worker process (perhaps as global), subsequent calls to Pool.map shouldn't be effected (referencing Antoine's & Michael's points that "multiprocessing encapsulates each subprocesses globals in a separate namespace"). @Antoine - I'm making an effort to take everything you've said into consideration here. My initial PR and talk <https://www.youtube.com/watch?v=DH0JVSXvxu0> was intended to shed light on a couple of pitfalls that I often see Python end-users encounter with Pool. Moving beyond my naive first attempt, and the onslaught of deserved criticism, it seems that we have an opportunity here: No changes to the interface, just an optimization to reduce the frequency of pickling. Raymond Hettinger may also be interested in this optimization, as he speaks (with great analogies) about different ways you can misuse concurrency in Python <https://www.youtube.com/watch?v=9zinZmE3Ogk>. This would address one of the pitfalls that he outlines: the "size of the serialized/deserialized data". Is this an optimization that either of you would be willing to review, and accept, if I find there is a *reasonable way* to implement it? On Fri, Oct 12, 2018 at 3:40 PM Nathaniel Smith <n...@pobox.com> wrote: > On Fri, Oct 12, 2018, 06:09 Antoine Pitrou <solip...@pitrou.net> wrote: > >> On Fri, 12 Oct 2018 08:33:32 -0400 >> Sean Harrington <seanhar...@gmail.com> wrote: >> > Hi Nathaniel - this if this solution can be made performant, than I >> would >> > be more than satisfied. >> > >> > I think this would require removing "func" from the "task tuple", and >> > storing the "func" "once per worker" somewhere globally (maybe a class >> > attribute set post-fork?). >> > >> > This also has the beneficial outcome of increasing general performance >> of >> > Pool.map and friends. I've seen MANY folks across the interwebs doing >> > things like passing instance methods to map, resulting in "big" tasks, >> and >> > slower-than-sequential parallelized code. Parallelizing "instance >> methods" >> > by passing them to map, w/o needing to wrangle with staticmethods and >> > globals, would be a GREAT feature! It'd just be as easy as: >> > >> > Pool.map(self.func, ls) >> > >> > What do you think about this idea? This is something I'd be able to take >> > on, assuming I get a few core dev blessings... >> >> Well, I'm not sure how it would work, so it's difficult to give an >> opinion. How do you plan to avoid passing "self"? By caching (by >> equality? by identity?)? Something else? But what happens if "self" >> changed value (in the case of a mutable object) in the parent? Do you >> keep using the stale version in the child? That would break >> compatibility... >> > > I was just suggesting that within a single call to Pool.map, it would be > reasonable optimization to only send the fn once to each worker. So e.g. if > you have 5 workers and 1000 items, you'd only pickle fn 5 times, rather > than 1000 times like we do now. I wouldn't want to get any fancier than > that with caching data between different map calls or anything. > > Of course even this may turn out to be too complicated to implement in a > reasonable way, since it would require managing some extra state on the > workers. But semantically it would be purely an optimization of current > semantics. > > -n > >> _______________________________________________ > Python-Dev mailing list > Python-Dev@python.org > https://mail.python.org/mailman/listinfo/python-dev > Unsubscribe: > https://mail.python.org/mailman/options/python-dev/seanharr11%40gmail.com >
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