ACCLAiM: Advancing the Practicality of MPI Collective Communication Autotuning Using Machine Learning-Reference-Cited by-同舟云学术

ACCLAiM: Advancing the Practicality of MPI Collective Communication Autotuning Using Machine Learning

Published:2022-09 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Cluster Computing (CLUSTER)
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Author:

Wilkins Michael¹,Guo Yanfei²,Thakur Rajeev²,Dinda Peter¹,Hardavellas Nikos¹

Affiliation:

1. Northwestern University

2. Argonne National Laboratory

Funder

National Nuclear Security Administration

U.S. Department of Energy

U.S. National Science Foundation

Argonne National Laboratory

Publisher

IEEE

Link

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Application-Agnostic Auto-Tuning of Open MPI Collectives Using Bayesian Optimization;2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW);2024-05-27

2. PML-MPI: A Pre-Trained ML Framework for Efficient Collective Algorithm Selection in MPI;2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW);2024-05-27

4. Generalized Collective Algorithms for the Exascale Era;2023 IEEE International Conference on Cluster Computing (CLUSTER);2023-10-31

5. OMPICollTune: Autotuning MPI Collectives by Incremental Online Learning;2022 IEEE/ACM International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS);2022-11