Article

A practical method for quickly evaluating program optimizations

Authors:

Grigori Fursin,

Michael O'Boyle,

Olivier TemamAuthors Info & Claims

HiPEAC'05: Proceedings of the First international conference on High Performance Embedded Architectures and Compilers

Pages 29 - 46

https://doi.org/10.1007/11587514_4

Published: 17 November 2005 Publication History

Abstract

This article aims at making iterative optimization practical and usable by speeding up the evaluation of a large range of optimizations. Instead of using a full run to evaluate a single program optimization, we take advantage of periods of stable performance, called phases. For that purpose, we propose a low-overhead phase detection scheme geared toward fast optimization space pruning, using code instrumentation and versioning implemented in a production compiler.

Our approach is driven by simplicity and practicality. We show that a simple phase detection scheme can be sufficient for optimization space pruning. We also show it is possible to search for complex optimizations at run-time without resorting to sophisticated dynamic compilation frameworks. Beyond iterative optimization, our approach also enables one to quickly design self-tuned applications.

Considering 5 representative SpecFP2000 benchmarks, our approach speeds up iterative search for the best program optimizations by a factor of 32 to 962. Phase prediction is 99.4% accurate on average, with an overhead of only 2.6%. The resulting self-tuned implementations bring an average speed-up of 1.4.

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Cited By

Mosaner RBarany GLeopoldseder DMössenböck HKotselidis CProkopec A(2022)Improving Vectorization Heuristics in a Dynamic Compiler with Machine Learning ModelsProceedings of the 14th ACM SIGPLAN International Workshop on Virtual Machines and Intermediate Languages10.1145/3563838.3567679(36-47)Online publication date: 29-Nov-2022
https://dl.acm.org/doi/10.1145/3563838.3567679
Mosaner RLeopoldseder DKisling WStadler LMössenböck H(2022)Machine-Learning-Based Self-Optimizing Compiler Heuristics✱Proceedings of the 19th International Conference on Managed Programming Languages and Runtimes10.1145/3546918.3546921(98-111)Online publication date: 14-Sep-2022
https://dl.acm.org/doi/10.1145/3546918.3546921
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
Show More Cited By

Index Terms

A practical method for quickly evaluating program optimizations
1. General and reference
  1. Cross-computing tools and techniques
    1. Performance
2. 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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Information & Contributors

Information

Published In

cover image Guide Proceedings

HiPEAC'05: Proceedings of the First international conference on High Performance Embedded Architectures and Compilers

November 2005

317 pages

ISBN:3540303170

Editors:
Tom Conte
NC State University,
,
Nacho Navarro
UPC, Spain
,
Wen-mei W. Hwu
Center for Reliable and High-Performance Computing and Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign,
,
Mateo Valero
Center for Reliable and High-Performance Computing and Department of Electrical and Computer Engineering, Barcelona Supercomputing Center-CNS, Spain
,
Theo Ungerer
Department of Computer Science, University of Augsburg, Augsburg, Germany

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 17 November 2005

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Cited By

Mosaner RBarany GLeopoldseder DMössenböck HKotselidis CProkopec A(2022)Improving Vectorization Heuristics in a Dynamic Compiler with Machine Learning ModelsProceedings of the 14th ACM SIGPLAN International Workshop on Virtual Machines and Intermediate Languages10.1145/3563838.3567679(36-47)Online publication date: 29-Nov-2022
https://dl.acm.org/doi/10.1145/3563838.3567679
Mosaner RLeopoldseder DKisling WStadler LMössenböck H(2022)Machine-Learning-Based Self-Optimizing Compiler Heuristics✱Proceedings of the 19th International Conference on Managed Programming Languages and Runtimes10.1145/3546918.3546921(98-111)Online publication date: 14-Sep-2022
https://dl.acm.org/doi/10.1145/3546918.3546921
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
Fursin GMiceli RLokhmotov AGerndt MBaboulin MMalony AChamski ZNovillo DDel Vento D(2014)Collective mindScientific Programming10.1155/2014/79734822:4(309-329)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.1155/2014/797348
Ravichandran KPande S(2013)MultiverseACM SIGPLAN Notices10.1145/2544173.250952548:10(533-552)Online publication date: 29-Oct-2013
https://dl.acm.org/doi/10.1145/2544173.2509525
Ravichandran KPande SHosking AEugster PLopes C(2013)MultiverseProceedings of the 2013 ACM SIGPLAN international conference on Object oriented programming systems languages & applications10.1145/2509136.2509525(533-552)Online publication date: 29-Oct-2013
https://dl.acm.org/doi/10.1145/2509136.2509525
Cui HXue JWang LYang YFeng XFan D(2012)Extendable pattern-oriented optimization directivesACM Transactions on Architecture and Code Optimization10.1145/2355585.23555879:3(1-37)Online publication date: 5-Oct-2012
https://dl.acm.org/doi/10.1145/2355585.2355587
Tartara MCrespi Reghizzi SFedak GFox GDelamare S(2012)Parallel iterative compilationProceedings of third international workshop on MapReduce and its Applications Date10.1145/2287016.2287023(33-40)Online publication date: 18-Jun-2012
https://dl.acm.org/doi/10.1145/2287016.2287023
Chen YFang SEeckhout LTemam OWu C(2012)Iterative optimization for the data centerACM SIGPLAN Notices10.1145/2248487.215098347:4(49-60)Online publication date: 3-Mar-2012
https://dl.acm.org/doi/10.1145/2248487.2150983
Chen YFang SEeckhout LTemam OWu C(2012)Iterative optimization for the data centerACM SIGARCH Computer Architecture News10.1145/2189750.215098340:1(49-60)Online publication date: 3-Mar-2012
https://dl.acm.org/doi/10.1145/2189750.2150983
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