Performance Characterization of Python Runtimes for Multi-device Task Parallel Programming

Modern Python programs in high-performance computing call into compiled libraries and kernels for performance-critical tasks. However, effectively parallelizing these finer-grained, and often dynamic, kernels across modern heterogeneous platforms remains a challenge. This paper designs and optimizes...

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Vydáno v:	International journal of parallel programming Ročník 53; číslo 2; s. 16
Hlavní autoři:	Ruys, William, Lee, Hochan, You, Bozhi, Talati, Shreya, Park, Jaeyoung, Almgren-Bell, James, Yan, Yineng, Fernando, Milinda, Erez, Mattan, Gligoric, Milos, Burtscher, Martin, Rossbach, Christopher J., Pingali, Keshav, Biros, George
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.04.2025 Springer Nature B.V
Témata:	Computer Science Graphs Interpreters Libraries Parallel programming Processor Architectures Python Run time (computers) Software Engineering/Programming and Operating Systems Theory of Computation Workloads HPC in Python Task parallel programming GPU tasking systems GPU programming in Python Global Interpreter Lock
ISSN:	0885-7458, 1573-7640
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Abstract	Modern Python programs in high-performance computing call into compiled libraries and kernels for performance-critical tasks. However, effectively parallelizing these finer-grained, and often dynamic, kernels across modern heterogeneous platforms remains a challenge. This paper designs and optimizes a multi-threaded runtime for Python tasks on single-node multi-GPU systems, including tasks that use resources across multiple devices. We perform an experimental study which examines the impact of Python’s Global Interpreter Lock (GIL) on runtime performance and the potential gains under a GIL-less PEP703 future. This work explores tasks with variants for different different device sets, introducing new programming abstractions and runtime mechanisms to simplify their management and enhance portability. Our experimental analysis, using tasks graphs from synthetic and real applications, shows at least a 3 × (and up to 6 × ) performance improvement over its predecessor in scenarios with high GIL contention. Our implementation of multi-device tasks achieves 8 × less overhead per task relative to a multi-process alternative using Ray.
AbstractList	Modern Python programs in high-performance computing call into compiled libraries and kernels for performance-critical tasks. However, effectively parallelizing these finer-grained, and often dynamic, kernels across modern heterogeneous platforms remains a challenge. This paper designs and optimizes a multi-threaded runtime for Python tasks on single-node multi-GPU systems, including tasks that use resources across multiple devices. We perform an experimental study which examines the impact of Python’s Global Interpreter Lock (GIL) on runtime performance and the potential gains under a GIL-less PEP703 future. This work explores tasks with variants for different different device sets, introducing new programming abstractions and runtime mechanisms to simplify their management and enhance portability. Our experimental analysis, using tasks graphs from synthetic and real applications, shows at least a 3× (and up to 6×) performance improvement over its predecessor in scenarios with high GIL contention. Our implementation of multi-device tasks achieves 8× less overhead per task relative to a multi-process alternative using Ray. Modern Python programs in high-performance computing call into compiled libraries and kernels for performance-critical tasks. However, effectively parallelizing these finer-grained, and often dynamic, kernels across modern heterogeneous platforms remains a challenge. This paper designs and optimizes a multi-threaded runtime for Python tasks on single-node multi-GPU systems, including tasks that use resources across multiple devices. We perform an experimental study which examines the impact of Python’s Global Interpreter Lock (GIL) on runtime performance and the potential gains under a GIL-less PEP703 future. This work explores tasks with variants for different different device sets, introducing new programming abstractions and runtime mechanisms to simplify their management and enhance portability. Our experimental analysis, using tasks graphs from synthetic and real applications, shows at least a 3 × (and up to 6 × ) performance improvement over its predecessor in scenarios with high GIL contention. Our implementation of multi-device tasks achieves 8 × less overhead per task relative to a multi-process alternative using Ray.
ArticleNumber	16
Author	Gligoric, Milos Rossbach, Christopher J. Ruys, William Yan, Yineng Fernando, Milinda Erez, Mattan Talati, Shreya Park, Jaeyoung Lee, Hochan You, Bozhi Biros, George Pingali, Keshav Almgren-Bell, James Burtscher, Martin
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SubjectTerms	Computer Science Graphs Interpreters Libraries Parallel programming Processor Architectures Python Run time (computers) Software Engineering/Programming and Operating Systems Theory of Computation Workloads
Title	Performance Characterization of Python Runtimes for Multi-device Task Parallel Programming
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