article

Inducing heuristics to decide whether to schedule

Authors:

J. Eliot B. MossAuthors Info & Claims

ACM SIGPLAN Notices, Volume 39, Issue 6

Pages 183 - 194

https://doi.org/10.1145/996893.996864

Published: 09 June 2004 Publication History

Abstract

Instruction scheduling is a compiler optimization that can improve program speed, sometimes by 10% or more, but it can also be expensive. Furthermore, time spent optimizing is more important in a Java just-in-time (JIT) compiler than in a traditional one because a JIT compiles code at run time, adding to the running time of the program. We found that, on any given block of code, instruction scheduling often does not produce significant benefit and sometimes degrades speed. Thus, we hoped that we could focus scheduling effort on those blocks that benefit from it.Using supervised learning we induced heuristics to predict which blocks benefit from scheduling. The induced function chooses, for each block, between list scheduling and not scheduling the block at all. Using the induced function we obtained over 90% of the improvement of scheduling every block but with less than 25% of the scheduling effort. When used in combination with profile-based adaptive optimization, the induced function remains effective but gives a smaller reduction in scheduling effort. Deciding when to optimize, and which optimization(s) to apply, is an important open problem area in compiler research. We show that supervised learning solves one of these problems well.

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Cited By

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
Liu SCui YJiang QWang QWu W(2018)An efficient tile size selection model based on machine learningJournal of Parallel and Distributed Computing10.1016/j.jpdc.2018.06.005121(27-41)Online publication date: Nov-2018
https://doi.org/10.1016/j.jpdc.2018.06.005
Ashouri ABignoli APalermo GSilvano CKulkarni SCavazos J(2017)MiCOMPACM Transactions on Architecture and Code Optimization10.1145/312445214:3(1-28)Online publication date: 6-Sep-2017
https://dl.acm.org/doi/10.1145/3124452
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Index Terms

Inducing heuristics to decide whether to schedule
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

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Published In

cover image ACM SIGPLAN Notices

ACM SIGPLAN Notices Volume 39, Issue 6

PLDI '04

May 2004

299 pages

ISSN:0362-1340

EISSN:1558-1160

DOI:10.1145/996893

Issue’s Table of Contents

PLDI '04: Proceedings of the ACM SIGPLAN 2004 conference on Programming language design and implementation
June 2004
310 pages
ISBN:1581138075
DOI:10.1145/996841
General Chair:
William Pugh
University of Maryland, College Park
,
Program Chair:
Craig Chambers
University of Washington

Copyright © 2004 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 June 2004

Published in SIGPLAN Volume 39, Issue 6

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Cited By

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
Liu SCui YJiang QWang QWu W(2018)An efficient tile size selection model based on machine learningJournal of Parallel and Distributed Computing10.1016/j.jpdc.2018.06.005121(27-41)Online publication date: Nov-2018
https://doi.org/10.1016/j.jpdc.2018.06.005
Ashouri ABignoli APalermo GSilvano CKulkarni SCavazos J(2017)MiCOMPACM Transactions on Architecture and Code Optimization10.1145/312445214:3(1-28)Online publication date: 6-Sep-2017
https://dl.acm.org/doi/10.1145/3124452
Ashouri APalermo GCavazos JSilvano CAshouri APalermo GCavazos JSilvano C(2017)BackgroundAutomatic Tuning of Compilers Using Machine Learning10.1007/978-3-319-71489-9_1(1-22)Online publication date: 23-Dec-2017
https://doi.org/10.1007/978-3-319-71489-9_1
Fonseca ACabral B(2013)ÆminiumGPU: An Intelligent Framework for GPU ProgrammingFacing the Multicore-Challenge III10.1007/978-3-642-35893-7_9(96-107)Online publication date: 2013
https://doi.org/10.1007/978-3-642-35893-7_9
Lokuciejewski PMarwedel PLokuciejewski PMarwedel P(2011)Machine Learning Techniques in Compiler DesignWorst-Case Execution Time Aware Compilation Techniques for Real-Time Systems10.1007/978-90-481-9929-7_6(159-195)Online publication date: 2011
https://doi.org/10.1007/978-90-481-9929-7_6
Lokuciejewski PMarwedel PLokuciejewski PMarwedel P(2011)WCET-Aware Assembly Level OptimizationsWorst-Case Execution Time Aware Compilation Techniques for Real-Time Systems10.1007/978-90-481-9929-7_5(131-157)Online publication date: 2011
https://doi.org/10.1007/978-90-481-9929-7_5
Shukla ATiwari RKala R(2010)ReferencesReal Life Applications of Soft Computing10.1201/EBK1439822876-b1(597-639)Online publication date: 12-Jul-2010
https://doi.org/10.1201/EBK1439822876-b1
de Mesmay FVoronenko YPuschel M(2010)Offline library adaptation using automatically generated heuristics2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS)10.1109/IPDPS.2010.5470479(1-10)Online publication date: Apr-2010
https://doi.org/10.1109/IPDPS.2010.5470479
Malik A(2010)Spatial Based Feature Generation for Machine Learning Based Optimization CompilationProceedings of the 2010 Ninth International Conference on Machine Learning and Applications10.1109/ICMLA.2010.147(925-930)Online publication date: 12-Dec-2010
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