AIIO: Using Artificial Intelligence for Job-Level and Automatic I/O Performance Bottleneck Diagnosis-Reference-Cited by-同舟云学术

AIIO: Using Artificial Intelligence for Job-Level and Automatic I/O Performance Bottleneck Diagnosis

Published:2023-08-07 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 32nd International Symposium on High-Performance Parallel and Distributed Computing
language:
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Author:

Dong Bin¹^ORCID,Bez Jean Luca¹^ORCID,Byna Suren²^ORCID

Affiliation:

1. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

2. The Ohio State University & Lawrence Berkeley National Laboratory, Columbus, OH, USA

Funder

the U.S. Department of Energy (DOE)

Exascale Computing Project

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3588195.3592986

Reference56 articles.

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3. Dorian C. Arnold Dong H. Ahn Bronis R. de Supinski Gregory L. Lee Barton P. Miller and Martin Schulz. 2007. Stack Trace Analysis for Large Scale Debugging. In IPDPS. 1--10. https://doi.org/10.1109/IPDPS.2007.370254

4. Jiwoo Bang Chungyong Kim Kesheng Wu Alex Sim Suren Byna Hanul Sung and Hyeonsang Eom. 2021. An In-Depth I/O Pattern Analysis in HPC Systems. In HiPC. 400--405. https://doi.org/10.1109/HiPC53243.2021.00056 10.1109/HiPC53243.2021.00056

5. Jiwoo Bang Chungyong Kim Kesheng Wu Alex Sim Suren Byna Hanul Sung and Hyeonsang Eom. 2021. An In-Depth I/O Pattern Analysis in HPC Systems. In HiPC. 400--405. https://doi.org/10.1109/HiPC53243.2021.00056