short-paper

APAT: an access pattern analysis tool for distributed arrays

Authors:

Engin Kayraklioglu,

Tarek El-GhazawiAuthors Info & Claims

CF '18: Proceedings of the 15th ACM International Conference on Computing Frontiers

Pages 248 - 251

https://doi.org/10.1145/3203217.3203266

Published: 08 May 2018 Publication History

Abstract

Distributed arrays reduce programming effort through implicit communication. However, relying solely on this abstraction causes fine-grained communication and performance overhead. A variety of optimization techniques can be used to mitigate such overheads. However, these techniques require a thorough understanding of how distributed arrays are accessed which can be very challenging in realistic use cases. We present Access Pattern Analysis Tool (APAT) for distributed arrays. APAT is a framework that can be integrated into language software stack to efficiently collect access logs and analyze them. We show that APAT can help discover optimization opportunities that can lead to up to 35% improvement.

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Cited By

Kayraklioglu EFavry EEl-Ghazawi T(2021)A Machine-Learning-Based Framework for Productive Locality ExploitationIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.305134832:6(1409-1424)Online publication date: 1-Jun-2021
https://doi.org/10.1109/TPDS.2021.3051348
Kayraklioglu EEl-Ghazawi T(2020)An Automated Machine Learning Approach for Data Locality Optimizations in Chapel2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW50202.2020.00113(671-671)Online publication date: May-2020
https://doi.org/10.1109/IPDPSW50202.2020.00113
Kayraklioglu EFavry EEl-Ghazawi T(2019)A Machine Learning Approach for Productive Data Locality Exploitation in Parallel Computing Systems2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)10.1109/CCGRID.2019.00050(361-370)Online publication date: May-2019
https://doi.org/10.1109/CCGRID.2019.00050

Index Terms

APAT: an access pattern analysis tool for distributed arrays
1. Computing methodologies
  1. Distributed computing methodologies
    1. Distributed programming languages
  2. Parallel computing methodologies
    1. Parallel programming languages

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cover image ACM Conferences

CF '18: Proceedings of the 15th ACM International Conference on Computing Frontiers

May 2018

401 pages

ISBN:9781450357616

DOI:10.1145/3203217

General Chairs:
David Kaeli
Northeastern University
,
Miquel Pericàs
Chalmers University of Technology, SE

Copyright © 2018 ACM.

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Published: 08 May 2018

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CF '18: Computing Frontiers Conference

May 8 - 10, 2018

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Cited By

Kayraklioglu EFavry EEl-Ghazawi T(2021)A Machine-Learning-Based Framework for Productive Locality ExploitationIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.305134832:6(1409-1424)Online publication date: 1-Jun-2021
https://doi.org/10.1109/TPDS.2021.3051348
Kayraklioglu EEl-Ghazawi T(2020)An Automated Machine Learning Approach for Data Locality Optimizations in Chapel2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW50202.2020.00113(671-671)Online publication date: May-2020
https://doi.org/10.1109/IPDPSW50202.2020.00113
Kayraklioglu EFavry EEl-Ghazawi T(2019)A Machine Learning Approach for Productive Data Locality Exploitation in Parallel Computing Systems2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)10.1109/CCGRID.2019.00050(361-370)Online publication date: May-2019
https://doi.org/10.1109/CCGRID.2019.00050

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