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An In-Depth I/O Pattern Analysis in HPC Systems

Cited 3 time in Web of Science Cited 2 time in Scopus

Bang, Jiwoo; Kim, Chungyong; Wu, Kesheng; Sim, Alex; Byna, Suren; Sung, Hanul; Eom, Hyeonsang

Issue Date
Institute of Electrical and Electronics Engineers Inc.
Proceedings - 2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics, HiPC 2021, pp.400-405
© 2021 IEEE.High-performance computing (HPC) systems consist of thousands of compute nodes, storage systems and high-speed networks, providing multiple layers of I/O stack with high complexity. By adjusting the diverse configuration settings that HPC systems provide, the I/O performance of applications can be improved. However, it is challenging to identify the optimal configuration settings without a thorough knowledge of the system, as each of the different I/O characteristics of applications can be an important factor for parameter decision. In this paper, we use multiple machine learning approaches to perform an in-depth analysis on I/O behaviors of HPC applications and to search for the optimal configuration settings for jobs sharing similar I/O characteristics. Improved by maximum 0.07 R-squared score, our results in overall show that jobs run on the HPC systems can obtain the predicted I/O performance for different configuration parameters with a high accuracy, using the proposed machine learning-based prediction models.
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