Article

Fast compiler optimisation evaluation using code-feature based performance prediction

Authors:

Christophe Dubach,

Grigori Fursin,

Michael F.P. O'Boyle,

Olivier TemamAuthors Info & Claims

CF '07: Proceedings of the 4th international conference on Computing frontiers

Pages 131 - 142

https://doi.org/10.1145/1242531.1242553

Published: 07 May 2007 Publication History

Abstract

Performance tuning is an important and time consuming task which may have to be repeated for each new application and platform. Although iterative optimisation can automate this process, it still requires many executions of different versions of the program. As execution time is frequently the limiting factor in the number of versions or transformed programs that can be considered, what is needed is a mechanism that can automatically predict the performance of a modified program without actually having to run it. This paper presents a new machine learning based technique to automatically predict the speedup of a modified program using a performance model based on the code features of the tuned programs. Unlike previous approaches it does not require any prior learning over a benchmark suite. Furthermore, it can be used to predict the performance of any tuning and is not restricted to a prior seen trans-formation space. We show that it can deliver predictions with a high correlation coefficient and can be used to dramatically reduce the cost of search.

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Cited By

Phothilimthana PAbu-El-Haija SCao KFatemi BBurrows MMendis CPerozzi BOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)TpuGraphsProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669205(70355-70375)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3669205
Seshadri KAkin BLaudon JNarayanaswami RYazdanbakhsh A(2022)An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks2022 IEEE International Symposium on Workload Characterization (IISWC)10.1109/IISWC55918.2022.00017(79-91)Online publication date: Nov-2022
https://doi.org/10.1109/IISWC55918.2022.00017
Sykora OPhothilimthana PMendis CYazdanbakhsh A(2022)GRANITE: A Graph Neural Network Model for Basic Block Throughput Estimation2022 IEEE International Symposium on Workload Characterization (IISWC)10.1109/IISWC55918.2022.00012(14-26)Online publication date: Nov-2022
https://doi.org/10.1109/IISWC55918.2022.00012
Show More Cited By

Index Terms

Fast compiler optimisation evaluation using code-feature based performance prediction
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Logical and relational learning
        Inductive logic learning
2. Software and its engineering
  1. Software notations and tools
    1. Compilers

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Information & Contributors

Information

Published In

cover image ACM Conferences

CF '07: Proceedings of the 4th international conference on Computing frontiers

May 2007

300 pages

ISBN:9781595936837

DOI:10.1145/1242531

General Chairs:
Utpal Banerjee
Intel, USA
,
José Moreira
IBM, USA
,
Program Chairs:
Michel Dubois
University of Southern California, USA
,
Per Stenström
Chalmers University of Technology, SE

Copyright © 2007 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: 07 May 2007

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May 7 - 9, 2007

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Phothilimthana PAbu-El-Haija SCao KFatemi BBurrows MMendis CPerozzi BOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)TpuGraphsProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669205(70355-70375)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3669205
Seshadri KAkin BLaudon JNarayanaswami RYazdanbakhsh A(2022)An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks2022 IEEE International Symposium on Workload Characterization (IISWC)10.1109/IISWC55918.2022.00017(79-91)Online publication date: Nov-2022
https://doi.org/10.1109/IISWC55918.2022.00017
Sykora OPhothilimthana PMendis CYazdanbakhsh A(2022)GRANITE: A Graph Neural Network Model for Basic Block Throughput Estimation2022 IEEE International Symposium on Workload Characterization (IISWC)10.1109/IISWC55918.2022.00012(14-26)Online publication date: Nov-2022
https://doi.org/10.1109/IISWC55918.2022.00012
You YSu Y(2022)Reduced O3 subsequence labelling: a stepping stone towards optimisation sequence predictionConnection Science10.1080/09540091.2022.204476134:1(2860-2877)Online publication date: 1-Mar-2022
https://doi.org/10.1080/09540091.2022.2044761
Kalyur SG.S N(2021)Inherent Parallelism and Speedup Estimation of Sequential ProgramsAnnals of Emerging Technologies in Computing10.33166/AETiC.2021.02.0065:2(62-77)Online publication date: 1-Apr-2021
https://doi.org/10.33166/AETiC.2021.02.006
Mosaner RLeopoldseder DStadler LMössenböck HKuchen HSinger J(2021)Using machine learning to predict the code size impact of duplication heuristics in a dynamic compilerProceedings of the 18th ACM SIGPLAN International Conference on Managed Programming Languages and Runtimes10.1145/3475738.3480943(127-135)Online publication date: 29-Sep-2021
https://dl.acm.org/doi/10.1145/3475738.3480943
Rasch ASchulze RSteuwer MGorlatch S(2021)Efficient Auto-Tuning of Parallel Programs with Interdependent Tuning Parameters via Auto-Tuning Framework (ATF)ACM Transactions on Architecture and Code Optimization10.1145/342709318:1(1-26)Online publication date: 20-Jan-2021
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Wu JXu JMeng XZhang HZhang Z(2021)Enabling Reliability-Driven Optimization Selection with Gate Graph Attention Neural NetworkInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402040024030:11n12(1641-1665)Online publication date: 21-Jan-2021
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Phothilimthana PSabne ASarda NMurthy KZhou YAngermueller CBurrows MRoy SMandke KFarahani RWang YIlbeyi BHechtman BRoune BWang SXu YKaufman S(2021)A Flexible Approach to Autotuning Multi-Pass Machine Learning Compilers2021 30th International Conference on Parallel Architectures and Compilation Techniques (PACT)10.1109/PACT52795.2021.00008(1-16)Online publication date: Sep-2021
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Remmelg THagedorn BLi LSteuwer MGorlatch SDubach CJog AKayiran OPattnaik A(2020)High-level hardware feature extraction for GPU performance prediction of stencilsProceedings of the 13th Annual Workshop on General Purpose Processing using Graphics Processing Unit10.1145/3366428.3380769(21-30)Online publication date: 23-Feb-2020
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