Article

Online Adaptive Code Generation and Tuning

Authors:

Ananta Tiwari,

Jeffrey K. HollingsworthAuthors Info & Claims

IPDPS '11: Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium

Pages 879 - 892

https://doi.org/10.1109/IPDPS.2011.86

Published: 16 May 2011 Publication History

Abstract

In this paper, we present a runtime compilation and tuning framework for parallel programs. We extend our prior work on our auto-tuner, Active Harmony, for tunable parameters that require code generation (for example, different unroll factors). For such parameters, our auto-tuner generates and compiles new code on-the-fly. Effectively, we merge traditional feedback directed optimization and just-in-time compilation. We show that our system can leverage available parallelism in today's HPC platforms by evaluating different code-variants on different nodes simultaneously. We evaluate our system on two parallel applications and show that our system can improve runtime execution by up to 46% compared to the original version of the program.

Cited By

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Randall TKoo JVideau BKruse MWu XHovland PHall MGe RBalaprakash PGallivan KNikolopoulos DBeivide RGallopoulos E(2023)Transfer-learning-based Autotuning using Gaussian CopulaProceedings of the 37th International Conference on Supercomputing10.1145/3577193.3593712(37-49)Online publication date: 21-Jun-2023
https://dl.acm.org/doi/10.1145/3577193.3593712
Roy RPatel TGadepally VTiwari DFreund SYahav E(2021)Bliss: auto-tuning complex applications using a pool of diverse lightweight learning modelsProceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation10.1145/3453483.3454109(1280-1295)Online publication date: 19-Jun-2021
https://dl.acm.org/doi/10.1145/3453483.3454109
Alappat CSeiferth JHager GKorch MRauber TWellein GLee J(2021)YaskSiteProceedings of the 2021 IEEE/ACM International Symposium on Code Generation and Optimization10.1109/CGO51591.2021.9370316(174-186)Online publication date: 27-Feb-2021
https://dl.acm.org/doi/10.1109/CGO51591.2021.9370316
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Index Terms

Online Adaptive Code Generation and Tuning
1. Software and its engineering
  1. Software notations and tools
    1. Compilers

Index terms have been assigned to the content through auto-classification.

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IPDPS '11: Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium

May 2011

1285 pages

ISBN:9780769543857

Publisher

IEEE Computer Society

United States

Publication History

Published: 16 May 2011

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Randall TKoo JVideau BKruse MWu XHovland PHall MGe RBalaprakash PGallivan KNikolopoulos DBeivide RGallopoulos E(2023)Transfer-learning-based Autotuning using Gaussian CopulaProceedings of the 37th International Conference on Supercomputing10.1145/3577193.3593712(37-49)Online publication date: 21-Jun-2023
https://dl.acm.org/doi/10.1145/3577193.3593712
Roy RPatel TGadepally VTiwari DFreund SYahav E(2021)Bliss: auto-tuning complex applications using a pool of diverse lightweight learning modelsProceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation10.1145/3453483.3454109(1280-1295)Online publication date: 19-Jun-2021
https://dl.acm.org/doi/10.1145/3453483.3454109
Alappat CSeiferth JHager GKorch MRauber TWellein GLee J(2021)YaskSiteProceedings of the 2021 IEEE/ACM International Symposium on Code Generation and Optimization10.1109/CGO51591.2021.9370316(174-186)Online publication date: 27-Feb-2021
https://dl.acm.org/doi/10.1109/CGO51591.2021.9370316
Xydis SChristoforidis ESoudris DLi Z(2020)DDOTProceedings of the 57th ACM/EDAC/IEEE Design Automation Conference10.5555/3437539.3437636(1-6)Online publication date: 20-Jul-2020
https://dl.acm.org/doi/10.5555/3437539.3437636
Seiferth JKorch MRauber T(2020)Offsite Autotuning ApproachHigh Performance Computing10.1007/978-3-030-50743-5_19(370-390)Online publication date: 22-Jun-2020
https://dl.acm.org/doi/10.1007/978-3-030-50743-5_19
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
Behzad BByna SPrabhat Snir M(2019)Optimizing I/O Performance of HPC Applications with AutotuningACM Transactions on Parallel Computing10.1145/33092055:4(1-27)Online publication date: 8-Mar-2019
https://dl.acm.org/doi/10.1145/3309205
Scherg MSeiferth JKorch MRauber TApte VDi Marco ALitoiu MMerseguer J(2019)Performance Prediction of Explicit ODE Methods on Multi-Core Cluster SystemsProceedings of the 2019 ACM/SPEC International Conference on Performance Engineering10.1145/3297663.3310306(139-150)Online publication date: 4-Apr-2019
https://dl.acm.org/doi/10.1145/3297663.3310306
Memeti SPllana SBinotto AKołodziej JBrandic I(2019)Using meta-heuristics and machine learning for software optimization of parallel computing systemsComputing10.1007/s00607-018-0614-9101:8(893-936)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s00607-018-0614-9
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
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Generation of parallel synchronization-free tiled code

Distributed memory code generation for mixed Irregular/Regular computations

Routine run-time code generation

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