Publications
Detailed Information
An In-Depth I/O Pattern Analysis in HPC Systems
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Bang, Jiwoo | - |
dc.contributor.author | Kim, Chungyong | - |
dc.contributor.author | Wu, Kesheng | - |
dc.contributor.author | Sim, Alex | - |
dc.contributor.author | Byna, Suren | - |
dc.contributor.author | Sung, Hanul | - |
dc.contributor.author | Eom, Hyeonsang | - |
dc.date.accessioned | 2022-06-24T00:26:23Z | - |
dc.date.available | 2022-06-24T00:26:23Z | - |
dc.date.created | 2022-05-09 | - |
dc.date.issued | 2021-01 | - |
dc.identifier.citation | Proceedings - 2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics, HiPC 2021, pp.400-405 | - |
dc.identifier.issn | 1094-7256 | - |
dc.identifier.uri | https://hdl.handle.net/10371/183768 | - |
dc.description.abstract | © 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. | - |
dc.language | 영어 | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | An In-Depth I/O Pattern Analysis in HPC Systems | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/HiPC53243.2021.00056 | - |
dc.citation.journaltitle | Proceedings - 2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics, HiPC 2021 | - |
dc.identifier.wosid | 000782316500043 | - |
dc.identifier.scopusid | 2-s2.0-85125625317 | - |
dc.citation.endpage | 405 | - |
dc.citation.startpage | 400 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Eom, Hyeonsang | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
- Appears in Collections:
- Files in This Item:
- There are no files associated with this item.
Item View & Download Count
Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.