Python shared atomic data types

Although atomicity plays a key role in data operations of shared variables in parallel computation, researchers haven't treated atomicity in Python in much detail. This study provides a novel approach to integrate the CPU‐based atomic C APIs into Python shared variables by C Foreign Function In...

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Bibliographic Details
Published in:Software, practice & experience Vol. 53; no. 12; pp. 2393 - 2407
Main Author: Ren, Xiquan
Format: Journal Article
Language:English
Published: Bognor Regis Wiley Subscription Services, Inc 01.12.2023
Subjects:
Computation
Concurrent processing
Parallel processing
ISSN:0038-0644, 1097-024X
Online Access:Get full text
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Summary:Although atomicity plays a key role in data operations of shared variables in parallel computation, researchers haven't treated atomicity in Python in much detail. This study provides a novel approach to integrate the CPU‐based atomic C APIs into Python shared variables by C Foreign Function Interface for Python (CFFI) on all major platforms and utilises Cython to optimise calculation in CPython. Evidence shows that the resulting product, Shared Atomic Enterprise (SAE), could accelerate data operations on shared data types to a large extent. These findings provide a solid evidence base for the massive utilisation of Python atomic operations in parallel computation and concurrent programming.
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ISSN:0038-0644
1097-024X
DOI:10.1002/spe.3259