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Link to original content: https://doi.org/10.1145/2503210.2503247

There goes the neighborhood | Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

research-article

There goes the neighborhood: performance degradation due to nearby jobs

Authors:

Abhinav Bhatele,

Kathryn Mohror,

Steven H. Langer,

Katherine E. IsaacsAuthors Info & Claims

SC '13: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

Article No.: 41, Pages 1 - 12

https://doi.org/10.1145/2503210.2503247

Published: 17 November 2013 Publication History

Abstract

Predictable performance is important for understanding and alleviating application performance issues; quantifying the effects of source code, compiler, or system software changes; estimating the time required for batch jobs; and determining the allocation requests for proposals. Our experiments show that on a Cray XE system, the execution time of a communication-heavy parallel application ranges from 28% faster to 41% slower than the average observed performance. Blue Gene systems, on the other hand, demonstrate no noticeable run-to-run variability. In this paper, we focus on Cray machines and investigate potential causes for performance variability such as OS jitter, shape of the allocated partition, and interference from other jobs sharing the same network links. Reducing such variability could improve overall throughput at a computer center and save energy costs.

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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
Aggarwal SMalakar P(2024)Evaluating Active-learning Based Performance Prediction of Parallel Applications2024 IEEE 20th International Conference on e-Science (e-Science)10.1109/e-Science62913.2024.10678665(1-10)Online publication date: 16-Sep-2024
https://doi.org/10.1109/e-Science62913.2024.10678665
Aksar BSencan ESchwaller BAaziz OLeung VBrandt JKulis BEgele MCoskun A(2024)Runtime Performance Anomaly Diagnosis in Production HPC Systems Using Active LearningIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.336546235:4(693-706)Online publication date: Apr-2024
https://doi.org/10.1109/TPDS.2024.3365462
Show More Cited By

Index Terms

There goes the neighborhood: performance degradation due to nearby jobs
1. General and reference
  1. Cross-computing tools and techniques
2. Networks
  1. Network properties
    1. Network structure

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cover image ACM Conferences

SC '13: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

November 2013

1123 pages

ISBN:9781450323789

DOI:10.1145/2503210

General Chair:
William Gropp
University of Illinois at Urbana-Champaign, Urbana, Illinois
,
Program Chair:
Satoshi Matsuoka
Tokyo Institute of Technology, Tokyo, Japan

Copyright © 2013 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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Published: 17 November 2013

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SC13: International Conference for High Performance Computing, Networking, Storage and Analysis

November 17 - 21, 2013

Colorado, Denver

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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
Aggarwal SMalakar P(2024)Evaluating Active-learning Based Performance Prediction of Parallel Applications2024 IEEE 20th International Conference on e-Science (e-Science)10.1109/e-Science62913.2024.10678665(1-10)Online publication date: 16-Sep-2024
https://doi.org/10.1109/e-Science62913.2024.10678665
Aksar BSencan ESchwaller BAaziz OLeung VBrandt JKulis BEgele MCoskun A(2024)Runtime Performance Anomaly Diagnosis in Production HPC Systems Using Active LearningIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.336546235:4(693-706)Online publication date: Apr-2024
https://doi.org/10.1109/TPDS.2024.3365462
Huerta Y(2024)Performance Analysis of the NVIDIA HPC SDK and AMD AOCC Compilers in an HPC Cluster Using Pooled, Robust and Relative Metrics2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW63119.2024.00135(726-737)Online publication date: 27-May-2024
https://doi.org/10.1109/IPDPSW63119.2024.00135
Copik MChrapek MSchmid LCalotoiu AHoefler T(2024)Software Resource Disaggregation for HPC with Serverless Computing2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS57955.2024.00021(139-156)Online publication date: 27-May-2024
https://doi.org/10.1109/IPDPS57955.2024.00021
Nazarov ALapatin ILizyura O(2024)Approbation of Asymptotic Method for Queue with an Unlimited Number of Servers and State-Dependent Service RateDistributed Computer and Communication Networks: Control, Computation, Communications10.1007/978-3-031-50482-2_28(361-372)Online publication date: 24-Mar-2024
https://doi.org/10.1007/978-3-031-50482-2_28
Cui MPapadopoulou NPericàs M(2023)Analysis and Characterization of Performance Variability for OpenMP RuntimeProceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis10.1145/3624062.3624239(1614-1622)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3624062.3624239
Siefert CPearson COlivier SProkopenko AHu JFuller T(2023)Latency and Bandwidth Microbenchmarks of US Department of Energy Systems in the June 2023 Top 500 ListProceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis10.1145/3624062.3624203(1298-1305)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3624062.3624203
Huck KMalony A(2023)ZeroSum: User Space Monitoring of Resource Utilization and Contention on Heterogeneous HPC SystemsProceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis10.1145/3624062.3624145(685-695)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3624062.3624145
Aksar BSencan ESchwaller BAaziz OLeung VBrandt JKulis BEgele MCoskun AMohror KArnold DBadia R(2023)Prodigy: Towards Unsupervised Anomaly Detection in Production HPC SystemsProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3581784.3607076(1-14)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3581784.3607076
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