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Determining average program execution times and their variance

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V. SarkarAuthors Info & Claims

PLDI '89: Proceedings of the ACM SIGPLAN 1989 conference on Programming language design and implementation

Pages 298 - 312

https://doi.org/10.1145/73141.74845

Published: 21 June 1989 Publication History

Abstract

This paper presents a general framework for determining average program execution times and their variance, based on the program's interval structure and control dependence graph. Average execution times and variance values are computed using frequency information from an optimized counter-based execution profile of the program.

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Determining average program execution times and their variance

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Published In

cover image ACM Conferences

PLDI '89: Proceedings of the ACM SIGPLAN 1989 conference on Programming language design and implementation

June 1989

355 pages

ISBN:089791306X

DOI:10.1145/73141

Editor:
R. L. Wexelblat
ACM Headquarters, New York, NY

ACM SIGPLAN Notices Volume 24, Issue 7
Proceedings of the SIGPLAN '89 symposium on Interpreters and interpretive techniques
July 1989
355 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/74818
Editor:
Richard Wexelblat
Issue’s Table of Contents

Copyright © 1989 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: 21 June 1989

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PLDI89: Programming Language Design & Implementation

June 19 - 23, 1989

Oregon, Portland, USA

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Overall Acceptance Rate 406 of 2,067 submissions, 20%

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https://dl.acm.org/doi/10.1145/3624309.3624311
Wang ZBai LLiu XChen YZhao MTao J(2022)Dynamic Task Scheduling in Remote Sensing Data Acquisition from Open-Access Data Using CloudSimApplied Sciences10.3390/app12221150812:22(11508)Online publication date: 12-Nov-2022
https://doi.org/10.3390/app122211508
Mururu GRavichandran KGavrilovska APande S(2020)Generating Robust Parallel Programs via Model Driven Prediction of Compiler Optimizations for Non-determinismProceedings of the 49th International Conference on Parallel Processing10.1145/3404397.3404464(1-12)Online publication date: 17-Aug-2020
https://dl.acm.org/doi/10.1145/3404397.3404464
Klusáček DChlumský V(2019)Evaluating the Impact of Soft Walltimes on Job Scheduling PerformanceJob Scheduling Strategies for Parallel Processing10.1007/978-3-030-10632-4_2(15-38)Online publication date: 13-Jan-2019
https://doi.org/10.1007/978-3-030-10632-4_2
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