GPU-aware Communication with UCX in Parallel Programming Models: Charm++, MPI, and Python

As an increasing number of leadership-class systems embrace GPU accelerators in the race towards exascale, efficient communication of GPU data is becoming one of the most critical components of high-performance computing. For developers of parallel programming models, implementing support for GPU-aw...

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Bibliographic Details
Published in:2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) pp. 479 - 488
Main Authors: Choi, Jaemin, Fink, Zane, White, Sam, Bhat, Nitin, Richards, David F., Kale, Laxmikant V.
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2021
Subjects:
Adaptation models
AMPI
Bandwidth
Biological system modeling
Charm
Charm4py
CUDA-aware MPI
GPU communication
Graphics processing units
Parallel programming
Python
Runtime
Semantics
UCX
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Summary:As an increasing number of leadership-class systems embrace GPU accelerators in the race towards exascale, efficient communication of GPU data is becoming one of the most critical components of high-performance computing. For developers of parallel programming models, implementing support for GPU-aware communication using native APIs for GPUs such as CUDA can be a daunting task as it requires considerable effort with little guarantee of performance. In this work, we demonstrate the capability of the Unified Communication X (UCX) framework to compose a GPU-aware communication layer that serves multiple parallel programming models of the Charm++ ecosystem: Charm++, Adaptive MPI (AMPI), and Charm4py. We demonstrate the performance impact of our designs with microbenchmarks adapted from the OSU benchmark suite, obtaining improvements in latency of up to 10.2x, 11.7x, and 17.4x in Charm++, AMPI, and Charm4py, respectively. We also observe increases in bandwidth of up to 9.6x in Charm++, 10x in AMPI, and 10.5x in Charm4py. We show the potential impact of our designs on real-world applications by evaluating a proxy application for the Jacobi iterative method, improving the communication performance by up to 12.4x in Charm++, 12.8x in AMPI, and 19.7x in Charm4py.
DOI:10.1109/IPDPSW52791.2021.00079