research-article

FuncyTuner: Auto-tuning Scientific Applications With Per-loop Compilation

Authors:

David Beckingsale,

Todd GamblinAuthors Info & Claims

ICPP '19: Proceedings of the 48th International Conference on Parallel Processing

Article No.: 91, Pages 1 - 10

https://doi.org/10.1145/3337821.3337842

Published: 05 August 2019 Publication History

Abstract

The de facto compilation model for production software compiles all modules of a target program with a single set of compilation flags, typically 02 or 03. Such a per-program compilation strategy may yield sub-optimal executables since programs often have multiple hot loops with diverse code structures and may be better optimized with a per-region compilation model that assembles an optimized executable by combining the best per-region code variants.

In this paper, we demonstrate that a naïve greedy approach to per-region compilation often degrades performance in comparison to the 03 baseline. To overcome this problem, we contribute a novel per-loop compilation framework, FuncyTuner, which employs lightweight profiling to collect per-loop timing information, and then utilizes a space-focusing technique to construct a performant executable. Experimental results show that FuncyTuner can reliably improve performance of modern scientific applications on several multi-core architectures by 9.2% to 12.3% and 4.5% to 10.7%(geometric mean, up to 22% on certain program) in comparison to the 03 baseline and prior work, respectively.

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Cited By

Yang TChen RLi YLiu XWang G(2023)CoTuner: A Hierarchical Learning Framework for Coordinately Optimizing Resource Partitioning and Parameter TuningProceedings of the 52nd International Conference on Parallel Processing10.1145/3605573.3605578(317-326)Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1145/3605573.3605578
Cereda SPalermo GCremonesi PDoni SXue JJung C(2020)A Collaborative Filtering Approach for the Automatic Tuning of Compiler OptimisationsThe 21st ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems10.1145/3372799.3394361(15-25)Online publication date: 16-Jun-2020
https://dl.acm.org/doi/10.1145/3372799.3394361

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cover image ACM Other conferences

ICPP '19: Proceedings of the 48th International Conference on Parallel Processing

August 2019

1107 pages

ISBN:9781450362955

DOI:10.1145/3337821

Copyright © 2019 ACM.

© 2019 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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University of Tsukuba: University of Tsukuba

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 August 2019

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ICPP 2019

ICPP 2019: 48th International Conference on Parallel Processing

August 5 - 8, 2019

Kyoto, Japan

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Overall Acceptance Rate 91 of 313 submissions, 29%

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Cited By

Yang TChen RLi YLiu XWang G(2023)CoTuner: A Hierarchical Learning Framework for Coordinately Optimizing Resource Partitioning and Parameter TuningProceedings of the 52nd International Conference on Parallel Processing10.1145/3605573.3605578(317-326)Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1145/3605573.3605578
Cereda SPalermo GCremonesi PDoni SXue JJung C(2020)A Collaborative Filtering Approach for the Automatic Tuning of Compiler OptimisationsThe 21st ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems10.1145/3372799.3394361(15-25)Online publication date: 16-Jun-2020
https://dl.acm.org/doi/10.1145/3372799.3394361

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