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Description:

pyarraypool 0.1.4

Pyarraypool

Transfer numpy array between processes using shared memory.
Why creating this project ?
This library aims to speed up parallel data processing with CPython and numpy NDArray.

What is the issue with regular python multitasking ?

Python GIL does not permit to use multithreading for parallel data processing.
It is indeed release when C code / Cython (nogil) / IO tasks are done but it is still lock for computation tasks.
This is why subprocess are often used to have multiple processing task done.
Alternative to subprocess worker exists but they are not always possible to use.
To list few of them:

numba with prange
switching from CPython to PyPy
rewrite code using C / Cython / Rust
using ray

Why not using numpy builtin mmap ?

Numpy builtin memory mapping is made to manage a single numpy array.
It is not made to manage multiple "small" array that are frequently created / destroy.

Why not using big framework like ray ?

Ray API is great / super simple / efficient.
Unfortunately it is slow because under the hood is still use pickle to transfer data between processes.
It is not optimized for local and CPU bound simple computation.
See:

performance comparison

Few design choices
Python standard library already contains a module to create and manage shared memory.
However it does not permit to manage it as a RAW bloc safely and easily.
So performances drop because several system call must be done on each bloc creation / deletion.
In this library:

shared memory is manage as a "pool" using Rust and low level CPython API.
array can be attached and are release when refcount reach 0 in every processes.
a spinlock is used to manage sync between process when bloc are add / removed (this can be improved).

API usage
Here a simple example of how to use library.
import pyarraypool
import multiprocessing
import numpy as np

def task(x, i, value):
# Define a dummy task than read and write to shared numpy array
x[i, :, :] = value

def main():
arr = np.random.random((100, 200, 500))
I, J, K = arr.shape

with multiprocessing.Pool(processes=8) as pool:
# Transfer the array to shared memory.
#
# Segment will be created automatically on first `make_transferable` call.
shmarr = pyarraypool.make_transferable(arr)

# Apply task to array
pool.starmap(task, [
(shmarr, i, i) for i in range(I)
])

if __name__ == "__main__":
main()

You can have a look at notebook / example folders for more details.
Fighting python GC
There is few things to know about array releasing.
When array are created in subprocess, GC can collect them before they are transfered in main process.
Exemple sequence:

Main process trigger subprocess job.
Subprocess has something to return and create a shared array in function return.
Subprocess GC is triggered and cleanup everything included return value.
So refcount reach 0.
Main process wakes up and try to get subprocess return value.
Value has already been collect by GC and release from pool => CRASH !

To fix this issue, there is a flag associated to each transferable array.
This flag is set when object has been transfered between 2 processes.
If this flag is not set, object will not be released.
In case you want to create a transferable object (but do not want to transfer it between processes), you
can override this flag using:
shmarr = pyarraypool.make_transferable(arr, transfer_required=False)

Object will be releas from array pool as soon as refcount reach 0.
Developper guide
To build:
pip install maturin
maturin develop --extras test

To test:
# Run rust tests
cargo test
cargo clippy

# Run python tests
pytest -vv
flake8
autopep8 --diff -r python/
mypy .

To format code:
autopep8 -ir python/
isort .

Project status
Project is currently a "POC" and not fully ready for production.
Few benchmark are still missing.
API can be improved and may change in near future.
See TODO.md for more details.
Any help / feedback is welcome 😊 !