research-article

Extracting clean performance models from tainted programs

Authors:

Alexandru Calotoiu,

Tobias Grosser,

Torsten HoeflerAuthors Info & Claims

PPoPP '21: Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

Pages 403 - 417

https://doi.org/10.1145/3437801.3441613

Published: 17 February 2021 Publication History

Abstract

Performance models are well-known instruments to understand the scaling behavior of parallel applications. They express how performance changes as key execution parameters, such as the number of processes or the size of the input problem, vary. Besides reasoning about program behavior, such models can also be automatically derived from performance data. This is called empirical performance modeling. While this sounds simple at the first glance, this approach faces several serious interrelated challenges, including expensive performance measurements, inaccuracies inflicted by noisy benchmark data, and overall complex experiment design, starting with the selection of the right parameters. The more parameters one considers, the more experiments are needed and the stronger the impact of noise. In this paper, we show how taint analysis, a technique borrowed from the domain of computer security, can substantially improve the modeling process, lowering its cost, improving model quality, and help validate performance models and experimental setups.

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Cited By

Cela SCiancone AGustafsson PHajdu ÁJia YKapus TKoshtenko MLewis WMao KMartac Dd'Amorim M(2024)Automated End-to-End Dynamic Taint Analysis for WhatsAppCompanion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering10.1145/3663529.3663824(21-26)Online publication date: 10-Jul-2024
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Schmid LSağlam TSelzer MKoziolek ALiem RAfzal AKousha PZhu ZLee J(2024)Cost-Efficient Construction of Performance ModelsProceedings of the 4th Workshop on Performance EngineeRing, Modelling, Analysis, and VisualizatiOn STrategy10.1145/3660317.3660322(1-7)Online publication date: 3-Jun-2024
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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
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Index Terms

Extracting clean performance models from tainted programs
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation
      2. Modeling methodologies
2. Theory of computation
  1. Models of computation
    1. Concurrency
  2. Semantics and reasoning
    1. Program semantics

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cover image ACM Conferences

PPoPP '21: Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

February 2021

507 pages

ISBN:9781450382946

DOI:10.1145/3437801

General Chair:
Jaejin Lee
Seoul National University, South Korea
,
Program Chair:
Erez Petrank
Technion, Israel

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PPoPP '21 Paper Acceptance Rate 31 of 150 submissions, 21%;

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

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https://dl.acm.org/doi/10.1145/3663529.3663824
Schmid LSağlam TSelzer MKoziolek ALiem RAfzal AKousha PZhu ZLee J(2024)Cost-Efficient Construction of Performance ModelsProceedings of the 4th Workshop on Performance EngineeRing, Modelling, Analysis, and VisualizatiOn STrategy10.1145/3660317.3660322(1-7)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3660317.3660322
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
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