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Title: Lightweight Function Monitors for Fine-Grained Management in Large Scale Python Applications
Python has become a widely used programming language for research, not only for small one-off analyses, but also for complex application pipelines running at supercomputer- scale. Modern parallel programming frameworks for Python present users with a more granular unit of management than traditional Unix processes and batch submissions: the Python function. We review the challenges involved in running native Python functions at scale, and present techniques for dynamically determining a minimal set of dependencies and for assembling a lightweight function monitor (LFM) that captures the software environment and manages resources at the granularity of single functions. We evaluate these techniques in a range of environ- ments, from campus cluster to supercomputer, and show that our advanced dependency management planning and dynamic re- source management methods provide superior performance and utilization relative to coarser-grained management approaches, achieving several-fold decrease in execution time for several large Python applications.  more » « less
Award ID(s):
1931348 2004932 2004894
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Date Published:
Journal Name:
IEEE International Parallel and Distributed Processing Symposium
Page Range / eLocation ID:
786 to 796
Medium: X
Sponsoring Org:
National Science Foundation
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