[Python-ideas] PEP 554: Stdlib Module to Support Multiple Interpreters in Python Code

[Matthew Rocklin]

Those numbers were for common use in Python tools and reflected my
anecdotal experience at the time with normal Python tools.  I'm sure that
there are mechanisms to achieve faster speeds than what I experienced.
That being said, here is a small example.


In [1]: import multiprocessing
In [2]: data = b'0' * 100000000  # 100 MB
In [3]: from toolz import identity
In [4]: pool = multiprocessing.Pool()
In [5]: %time _ = pool.apply_async(identity, (data,)).get()
CPU times: user 76 ms, sys: 64 ms, total: 140 ms
Wall time: 252 ms

This is about 400MB/s for a roundtrip


On Thu, Sep 7, 2017 at 9:00 PM, Stephan Hoyer <shoyer at gmail.com> wrote:

> On Thu, Sep 7, 2017 at 5:15 PM Nathaniel Smith <njs at pobox.com> wrote:
>
>> On Thu, Sep 7, 2017 at 4:23 PM, Nick Coghlan <ncoghlan at gmail.com> wrote:
>> > The gist of the idea is that with subinterpreters, your starting point
>> > is multiprocessing-style isolation (i.e. you have to use pickle to
>> > transfer data between subinterpreters), but you're actually running in
>> > a shared-memory threading context from the operating system's
>> > perspective, so you don't need to rely on mmap to share memory over a
>> > non-streaming interface.
>>
>> The challenge is that streaming bytes between processes is actually
>> really fast -- you don't really need mmap for that. (Maybe this was
>> important for X11 back in the 1980s, but a lot has changed since then
>> :-).) And if you want to use pickle and multiprocessing to send, say,
>> a single big numpy array between processes, that's also really fast,
>> because it's basically just a few memcpy's. The slow case is passing
>> complicated objects between processes, and it's slow because pickle
>> has to walk the object graph to serialize it, and walking the object
>> graph is slow. Copying object graphs between subinterpreters has the
>> same problem.
>>
>
> This doesn't match up with my (somewhat limited) experience. For example,
> in this table of bandwidth estimates from Matthew Rocklin (CCed), IPC is
> about 10x slower than a memory copy:
> http://matthewrocklin.com/blog/work/2015/12/29/data-bandwidth
>
> This makes a considerable difference when building a system do to parallel
> data analytics in Python (e.g., on NumPy arrays), which is exactly what
> Matthew has been working on for the past few years.
>
> I'm sure there are other ways to avoid this expensive IPC without using
> sub-interpreters, e.g., by using a tool like Plasma (
> http://arrow.apache.org/blog/2017/08/08/plasma-in-memory-object-store/).
> But I'm skeptical of your assessment that the current multiprocessing
> approach is fast enough.
>
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