Article

Active harmony: towards automated performance tuning

Authors:

Cristian Ţăpuş,

Jeffrey K. HollingsworthAuthors Info & Claims

SC '02: Proceedings of the 2002 ACM/IEEE conference on Supercomputing

Pages 1 - 11

Published: 16 November 2002 Publication History

Abstract

In this paper, we present the Active Harmony automated runtime tuning system. We describe the interface used by programs to make applications tunable. We present the Library Specification Layer which helps program library developers expose multiple variations of the same API using different algorithms. The Library Specification Language helps to select the most appropriate program library to tune the overall performance. We also present the optimization algorithm used to adjust parameters in the application and the libraries. Finally, we present results that show how the system is able to tune several real applications. The automated tuning system is able to tune the application parameters to within a few percent of the best value after evaluating only 11 out of over 1,700 possible configurations.

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Cited By

Alrabaee SDebbabi MWang L(2022)A Survey of Binary Code Fingerprinting Approaches: Taxonomy, Methodologies, and FeaturesACM Computing Surveys10.1145/348686055:1(1-41)Online publication date: 17-Jan-2022
https://dl.acm.org/doi/10.1145/3486860
Wagle BMonil MHuck KMalony ASerio AKaiser H(2019)Runtime Adaptive Task Inlining on Asynchronous Multitasking Runtime SystemsProceedings of the 48th International Conference on Parallel Processing10.1145/3337821.3337915(1-10)Online publication date: 5-Aug-2019
https://dl.acm.org/doi/10.1145/3337821.3337915
Kurzak JTsai YGates MAbdelfattah ADongarra J(2019)Massively Parallel Automated Software TuningProceedings of the 48th International Conference on Parallel Processing10.1145/3337821.3337908(1-10)Online publication date: 5-Aug-2019
https://dl.acm.org/doi/10.1145/3337821.3337908
Show More Cited By

Index Terms

Active harmony: towards automated performance tuning
1. General and reference
  1. Cross-computing tools and techniques
    1. Performance
2. Software and its engineering
  1. Software organization and properties
    1. Contextual software domains
      1. Operating systems

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Published In

cover image ACM Conferences

SC '02: Proceedings of the 2002 ACM/IEEE conference on Supercomputing

November 2002

952 pages

ISBN:076951524X

Conference Chair:
Roscoe Giles
Boston University
,
Program Chair:
Daniel Reed
NCSA
,
Publications Chair:
Kathryn Kelley
Ohio Supercomputer Center

Sponsors

SIGARCH: ACM Special Interest Group on Computer Architecture
IEEE-CS: Computer Society

Publisher

IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 16 November 2002

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SC '02

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SIGARCH
IEEE-CS

SC '02: International Conference for High Performance Computing, Networking, Storage and Analysis

16 11 2002

Maryland, Baltimore

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SC '02 Paper Acceptance Rate 67 of 230 submissions, 29%;

Overall Acceptance Rate 1,516 of 6,373 submissions, 24%

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Citations

Cited By

Alrabaee SDebbabi MWang L(2022)A Survey of Binary Code Fingerprinting Approaches: Taxonomy, Methodologies, and FeaturesACM Computing Surveys10.1145/348686055:1(1-41)Online publication date: 17-Jan-2022
https://dl.acm.org/doi/10.1145/3486860
Wagle BMonil MHuck KMalony ASerio AKaiser H(2019)Runtime Adaptive Task Inlining on Asynchronous Multitasking Runtime SystemsProceedings of the 48th International Conference on Parallel Processing10.1145/3337821.3337915(1-10)Online publication date: 5-Aug-2019
https://dl.acm.org/doi/10.1145/3337821.3337915
Kurzak JTsai YGates MAbdelfattah ADongarra J(2019)Massively Parallel Automated Software TuningProceedings of the 48th International Conference on Parallel Processing10.1145/3337821.3337908(1-10)Online publication date: 5-Aug-2019
https://dl.acm.org/doi/10.1145/3337821.3337908
Pfaffe PGrosser TTillmann MEigenmann RDing CMcKee S(2019)Efficient hierarchical online-autotuningProceedings of the ACM International Conference on Supercomputing10.1145/3330345.3330377(354-366)Online publication date: 26-Jun-2019
https://dl.acm.org/doi/10.1145/3330345.3330377
Liu JTan GLuo YLi JMo ZSun N(2019)An Autotuning Protocol to Rapidly Build AutotunersACM Transactions on Parallel Computing10.1145/32915275:2(1-25)Online publication date: 4-Jan-2019
https://dl.acm.org/doi/10.1145/3291527
Deiana ESt-Amour VDinda PHardavellas NCampanoni S(2018)Unconventional Parallelization of Nondeterministic ApplicationsACM SIGPLAN Notices10.1145/3296957.317318153:2(432-447)Online publication date: 19-Mar-2018
https://dl.acm.org/doi/10.1145/3296957.3173181
Alcaraz JSikora ACesar E(2018)Dynamic Tuning of OpenMP Memory Bound Applications in Multisocket Systems using MATEWorkshop Proceedings of the 47th International Conference on Parallel Processing10.1145/3229710.3229748(1-10)Online publication date: 13-Aug-2018
https://dl.acm.org/doi/10.1145/3229710.3229748
Thiagarajan JJain NAnirudh RGimenez ASridhar RMarathe AWang TEmani MBhatele AGamblin T(2018)Bootstrapping Parameter Space Exploration for Fast TuningProceedings of the 2018 International Conference on Supercomputing10.1145/3205289.3205321(385-395)Online publication date: 12-Jun-2018
https://dl.acm.org/doi/10.1145/3205289.3205321
Ashouri AKillian WCavazos JPalermo GSilvano C(2018)A Survey on Compiler Autotuning using Machine LearningACM Computing Surveys10.1145/319797851:5(1-42)Online publication date: 18-Sep-2018
https://dl.acm.org/doi/10.1145/3197978
Eljammaly MKarlsson LKågström BWolter KKnottenbelt Wvan Hoorn ANambiar MKoziolek H(2018)An Auto-Tuning Framework for a NUMA-Aware Hessenberg Reduction AlgorithmCompanion of the 2018 ACM/SPEC International Conference on Performance Engineering10.1145/3185768.3186304(5-8)Online publication date: 2-Apr-2018
https://dl.acm.org/doi/10.1145/3185768.3186304
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