Article

Methods of inference and learning for performance modeling of parallel applications

Authors:

Benjamin C. Lee,

David M. Brooks,

Bronis R. de Supinski,

Sally A. McKeeAuthors Info & Claims

PPoPP '07: Proceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming

Pages 249 - 258

https://doi.org/10.1145/1229428.1229479

Published: 14 March 2007 Publication History

Abstract

Increasing system and algorithmic complexity combined with a growing number of tunable application parameters pose significant challenges for analytical performance modeling. We propose a series of robust techniques to address these challenges. In particular, we apply statistical techniques such as clustering, association, and correlation analysis, to understand the application parameter space better. We construct and compare two classes of effective predictive models: piecewise polynomial regression and artifical neural networks. We compare these techniques with theoretical analyses and experimental results. Overall, both regression and neural networks are accurate with median error rates ranging from 2.2 to 10.5 percent. The comparable accuracy of these models suggest differentiating features will arise from ease of use, transparency, and computational efficiency.

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Cited By

Mahdavi K(2024)A Hybrid Machine Learning Method for Cross-Platform Performance Prediction of Parallel ApplicationsProceedings of the 53rd International Conference on Parallel Processing10.1145/3673038.3673059(669-678)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3673038.3673059
Li LFlynn THoisie A(2024)Learning Generalizable Program and Architecture Representations for Performance ModelingProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.1109/SC41406.2024.00072(1-15)Online publication date: 17-Nov-2024
https://dl.acm.org/doi/10.1109/SC41406.2024.00072
Dey ADhakal AIslam TYeom JPatki TNichols DMovsesyan ABhatele A(2024)Relative Performance Prediction Using Few-Shot Learning2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC61105.2024.00278(1764-1769)Online publication date: 2-Jul-2024
https://doi.org/10.1109/COMPSAC61105.2024.00278
Show More Cited By

Index Terms

Methods of inference and learning for performance modeling of parallel applications
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

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Information & Contributors

Information

Published In

cover image ACM Conferences

PPoPP '07: Proceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming

March 2007

284 pages

ISBN:9781595936028

DOI:10.1145/1229428

General Chair:
Katherine Yelick
UC Berkeley and Lawrence Berkeley National Lab., USA
,
Program Chair:
John Mellor-Crummey
Rice University, USA

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 14 March 2007

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PPoPP07: ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

March 14 - 17, 2007

California, San Jose, USA

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PPoPP '07 Paper Acceptance Rate 22 of 65 submissions, 34%;

Overall Acceptance Rate 230 of 1,014 submissions, 23%

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Cited By

Mahdavi K(2024)A Hybrid Machine Learning Method for Cross-Platform Performance Prediction of Parallel ApplicationsProceedings of the 53rd International Conference on Parallel Processing10.1145/3673038.3673059(669-678)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3673038.3673059
Li LFlynn THoisie A(2024)Learning Generalizable Program and Architecture Representations for Performance ModelingProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.1109/SC41406.2024.00072(1-15)Online publication date: 17-Nov-2024
https://dl.acm.org/doi/10.1109/SC41406.2024.00072
Dey ADhakal AIslam TYeom JPatki TNichols DMovsesyan ABhatele A(2024)Relative Performance Prediction Using Few-Shot Learning2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC61105.2024.00278(1764-1769)Online publication date: 2-Jul-2024
https://doi.org/10.1109/COMPSAC61105.2024.00278
Jung SYoo YYang GYoo C(2024)Prediction of permissioned blockchain performance for resource scaling configurationsICT Express10.1016/j.icte.2024.09.003Online publication date: Sep-2024
https://doi.org/10.1016/j.icte.2024.09.003
Smeliansky R(2023)Network Powered by Computing: Next Generation of Computational InfrastructureEdge Computing - Technology, Management and Integration10.5772/intechopen.110178Online publication date: 2-Aug-2023
https://doi.org/10.5772/intechopen.110178
Hutter ESolomonik EMohror KArnold DBadia R(2023)Application Performance Modeling via Tensor CompletionProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3581784.3607069(1-14)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3581784.3607069
Zhai JJin YChen WZheng WZhai JJin YChen WZheng W(2023)Performance Prediction for Scalability AnalysisPerformance Analysis of Parallel Applications for HPC10.1007/978-981-99-4366-1_6(129-161)Online publication date: 19-Jun-2023
https://doi.org/10.1007/978-981-99-4366-1_6
van Dijk JZavodszky GVarbanescu APimentel AHoekstra A(2023)Building a Fine-Grained Analytical Performance Model for Complex Scientific SimulationsParallel Processing and Applied Mathematics10.1007/978-3-031-30442-2_14(183-196)Online publication date: 28-Apr-2023
https://doi.org/10.1007/978-3-031-30442-2_14
Pandey SLi LFlynn THoisie ALiu HWolf FShende SCulhane CAlam SJagode H(2022)Scalable deep learning-based microarchitecture simulation on GPUsProceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis10.5555/3571885.3571990(1-15)Online publication date: 13-Nov-2022
https://dl.acm.org/doi/10.5555/3571885.3571990
Li LPandey SFlynn TLiu HWheeler NHoisie A(2022)SimNetProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/35308916:2(1-24)Online publication date: 6-Jun-2022
https://dl.acm.org/doi/10.1145/3530891
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