research-article

The I/O Trace Initiative: Building a Collaborative I/O Archive to Advance HPC

Authors:

André Brinkmann,

Marc-André Vef,

Philippe Deniel,

Jesus Carretero,

Jean-Thomas Acquaviva,

Reza SalkhordehAuthors Info & Claims

SC-W '23: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis

Pages 1216 - 1222

https://doi.org/10.1145/3624062.3624192

Published: 12 November 2023 Publication History

Abstract

HPC application developers and administrators need to understand the complex interplay between compute clusters and storage systems to make effective optimization decisions. Ad hoc investigations of this interplay based on isolated case studies can lead to conclusions that are incorrect or difficult to generalize. The I/O Trace Initiative aims to improve the scientific community’s understanding of I/O operations by building a searchable collaborative archive of I/O traces from a wide range of applications and machines, with a focus on high-performance computing and scalable AI/ML. This initiative advances the accessibility of I/O trace data by enabling users to locate and compare traces based on user-specified criteria. It also provides a visual analytics platform for in-depth analysis, paving the way for the development of advanced performance optimization techniques. By acting as a hub for trace data, the initiative fosters collaborative research by encouraging data sharing and collective learning.

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Recording of "The I/O Trace Initiative: Building a Collaborative I/O Archive to Advance HPC" presentation at PDSW 2023.

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Cited By

Egersdoerfer CSareen ABez JByna SDai D(2024)ION: Navigating the HPC I/O Optimization Journey using Large Language ModelsProceedings of the 16th ACM Workshop on Hot Topics in Storage and File Systems10.1145/3655038.3665950(86-92)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3655038.3665950
Medeiros DGregory ECouvee PHawkes JGougeaud SGilliot MBressand OValeri YJaeger JChapon DBournaud FStrafella LCaviedes-Voullième DTashakor GZjupa JHolicki MRidley TMüller YGuimarães FFrings WMirus JZhukov IBorba EMoti NSalkhordeh RDerbey NMimouni SDerr SGursoy BGrogan JFurmánek RGolasowski MSlaninová KMartinovič JFaltýnek JWong JCakircali MQuintino TSmart SIffrig ONarasimhamurthy SHapp SRauh MKrempel SWiggins MNováček JBrinkmann AMarkidis SDeniel P(2024)IO-SEA: Storage I/O and Data Management for Exascale ArchitecturesProceedings of the 21st ACM International Conference on Computing Frontiers: Workshops and Special Sessions10.1145/3637543.3654620(94-100)Online publication date: 7-May-2024
https://dl.acm.org/doi/10.1145/3637543.3654620
Hilgers TLiem R(2024)Comparability and Reproducibility in HPC Applications' Energy Consumption CharacterizationProceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems10.1145/3632775.3662162(560-568)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3632775.3662162

Index Terms

The I/O Trace Initiative: Building a Collaborative I/O Archive to Advance HPC
1. Computing methodologies
  1. Distributed computing methodologies
2. Information systems
  1. Information storage systems
    1. Storage management
      1. Hierarchical storage management

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cover image ACM Other conferences

SC-W '23: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis

November 2023

2180 pages

ISBN:9798400707858

DOI:10.1145/3624062

Copyright © 2023 ACM.

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Published: 12 November 2023

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SC-W 2023: Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis

November 12 - 17, 2023

CO, Denver, USA

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Cited By

Egersdoerfer CSareen ABez JByna SDai D(2024)ION: Navigating the HPC I/O Optimization Journey using Large Language ModelsProceedings of the 16th ACM Workshop on Hot Topics in Storage and File Systems10.1145/3655038.3665950(86-92)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3655038.3665950
Medeiros DGregory ECouvee PHawkes JGougeaud SGilliot MBressand OValeri YJaeger JChapon DBournaud FStrafella LCaviedes-Voullième DTashakor GZjupa JHolicki MRidley TMüller YGuimarães FFrings WMirus JZhukov IBorba EMoti NSalkhordeh RDerbey NMimouni SDerr SGursoy BGrogan JFurmánek RGolasowski MSlaninová KMartinovič JFaltýnek JWong JCakircali MQuintino TSmart SIffrig ONarasimhamurthy SHapp SRauh MKrempel SWiggins MNováček JBrinkmann AMarkidis SDeniel P(2024)IO-SEA: Storage I/O and Data Management for Exascale ArchitecturesProceedings of the 21st ACM International Conference on Computing Frontiers: Workshops and Special Sessions10.1145/3637543.3654620(94-100)Online publication date: 7-May-2024
https://dl.acm.org/doi/10.1145/3637543.3654620
Hilgers TLiem R(2024)Comparability and Reproducibility in HPC Applications' Energy Consumption CharacterizationProceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems10.1145/3632775.3662162(560-568)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3632775.3662162

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