research-article

Parallel Application Signature for Performance Analysis and Prediction

Authors:

Dolores Rexachs,

Emilio LuqueAuthors Info & Claims

IEEE Transactions on Parallel and Distributed Systems, Volume 26, Issue 7

Pages 2009 - 2019

https://doi.org/10.1109/TPDS.2014.2329688

Published: 01 July 2015 Publication History

Abstract

Predicting the performance of parallel scientific applications is becoming increasingly complex. Our goal was to characterize the behavior of message-passing applications on different target machines. To achieve this goal, we developed a method called parallel application signature for performance prediction (PAS2P), which strives to describe an application based on its behavior. Based on the application's message-passing activity, we identified and extracted representative phases, with which we created a parallel application signature that enabled us to predict the application's performance. We experimented with using different scientific applications on different clusters. We were able to predict execution times with an average accuracy greater than 97 percent.

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Rivas JWong ASuppi RLuque ERexachs D(2023)Efficiency Analysis for AI Applications in HPC Systems. Case Study: K-MeansComputational Science – ICCS 202310.1007/978-3-031-36021-3_39(373-380)Online publication date: 3-Jul-2023
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Tirado FWong ARexachs DLuque E(2022)Scalable performance analysis method for SPMD applicationsThe Journal of Supercomputing10.1007/s11227-022-04588-z78:17(19346-19371)Online publication date: 1-Nov-2022
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Flores-Contreras JDuran-Limon HChavoya AAlmanza-Ruiz S(2021)Performance prediction of parallel applications: a systematic literature reviewThe Journal of Supercomputing10.1007/s11227-020-03417-577:4(4014-4055)Online publication date: 1-Apr-2021
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cover image IEEE Transactions on Parallel and Distributed Systems

IEEE Transactions on Parallel and Distributed Systems Volume 26, Issue 7

July 2015

286 pages

ISSN:1045-9219

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Copyright © 2014.

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IEEE Press

Publication History

Published: 01 July 2015

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Cited By

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https://dl.acm.org/doi/10.1007/978-3-031-36021-3_39
Tirado FWong ARexachs DLuque E(2022)Scalable performance analysis method for SPMD applicationsThe Journal of Supercomputing10.1007/s11227-022-04588-z78:17(19346-19371)Online publication date: 1-Nov-2022
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Mariani GAnghel AJongerius RDittmann G(2018)Predicting cloud performance for HPC applications before deploymentFuture Generation Computer Systems10.1016/j.future.2017.10.04887:C(618-628)Online publication date: 1-Oct-2018
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