On 06Feb2019 0906, Antoine Pitrou wrote:
For the record there are number of initiatives currently to boost the
usefulness and efficiency of multi-process computation in Python.
One of them is PEP 574 (zero-copy pickling with out-of-band buffers),
which I'm working on.
Another is Pierre Glaser's work on allowing pickling of dynamic
functions and classes with the C-accelerated _pickle module (rather than
the slow pure Python implementation):
https://bugs.python.org/issue35900
https://bugs.python.org/issue35911
Another is Davin's work on shared memory managers.
There are also emerging standards like Apache Arrow that provide a
shared, runtime-agnostic, compute-friendly representation for in-memory
tabular data, and third-party frameworks like Dask which are
potentially able to work on top of that and expose nice end-user APIs.
For maximum synergy between these initiatives and the resulting APIs,
it is better if things are done in the open ;-)
Hopefully our steering council can determine (or delegate the
determination of) the direction we should go here so we can all be
pulling in the same direction :)
That said, there are certainly a number of interacting components and
not a lot of information about how they interact and overlap. A good
start would be to identify the likely overlap of this work to see where
they can build upon each other rather than competing, as well as
estimating the long-term burden of standardising.
Cheers,
Steve
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