Article

Scheduling straight-line code using reinforcement learning and rollouts

Authors:

Amy McGovern,

Eliot MossAuthors Info & Claims

Proceedings of the 1998 conference on Advances in neural information processing systems II

Pages 903 - 909

Published: 20 July 1999 Publication History

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Leather HBonilla EO'boyle M(2014)Automatic feature generation for machine learning--based optimising compilationACM Transactions on Architecture and Code Optimization10.1145/253668811:1(1-32)Online publication date: 1-Feb-2014
https://dl.acm.org/doi/10.1145/2536688
Dubach CJones TBonilla EFursin GO'Boyle MAlbonesi DMartonosi MAugust DMartínez J(2009)Portable compiler optimisation across embedded programs and microarchitectures using machine learningProceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture10.1145/1669112.1669124(78-88)Online publication date: 12-Dec-2009
https://dl.acm.org/doi/10.1145/1669112.1669124
Leather HBonilla EO'Boyle M(2009)Automatic Feature Generation for Machine Learning Based Optimizing CompilationProceedings of the 7th annual IEEE/ACM International Symposium on Code Generation and Optimization10.1109/CGO.2009.21(81-91)Online publication date: 22-Mar-2009
https://dl.acm.org/doi/10.1109/CGO.2009.21
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Index Terms

Scheduling straight-line code using reinforcement learning and rollouts
1. Computing methodologies
  1. Artificial intelligence
    1. Planning and scheduling
    2. Search methodologies
      1. Heuristic function construction
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Scheduling straight-line code using reinforcement learning and rollouts
NIPS'98: Proceedings of the 11th International Conference on Neural Information Processing Systems

The execution order of a block of computer instructions can make a difference in its running time by a factor of two or more. In order to achieve the best possible speed, compilers use heuristic schedulers appropriate to each specific architecture ...
Scheduling Straight-Line Code Using Reinforcement Learning and Rollouts
Building a Basic Block Instruction Scheduler with Reinforcement Learning and Rollouts

The execution order of a block of computer instructions on a pipelined machine can make a difference in running time by a factor of two or more. Compilers use heuristic schedulers appropriate to each specific architecture implementation to achieve the ...

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Published In

Proceedings of the 1998 conference on Advances in neural information processing systems II

July 1999

1090 pages

ISBN:0262112450

Chairmen:
Michael S. Kearns
AT&T Labs, Florham Park, NJ
,
Sara A. Solla
Northwestern Univ., Evanston, IL
,
Editor:
David A. Cohn
Harlequin, Inc.; and Just Research

Publisher

MIT Press

Cambridge, MA, United States

Publication History

Published: 20 July 1999

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Leather HBonilla EO'boyle M(2014)Automatic feature generation for machine learning--based optimising compilationACM Transactions on Architecture and Code Optimization10.1145/253668811:1(1-32)Online publication date: 1-Feb-2014
https://dl.acm.org/doi/10.1145/2536688
Dubach CJones TBonilla EFursin GO'Boyle MAlbonesi DMartonosi MAugust DMartínez J(2009)Portable compiler optimisation across embedded programs and microarchitectures using machine learningProceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture10.1145/1669112.1669124(78-88)Online publication date: 12-Dec-2009
https://dl.acm.org/doi/10.1145/1669112.1669124
Leather HBonilla EO'Boyle M(2009)Automatic Feature Generation for Machine Learning Based Optimizing CompilationProceedings of the 7th annual IEEE/ACM International Symposium on Code Generation and Optimization10.1109/CGO.2009.21(81-91)Online publication date: 22-Mar-2009
https://dl.acm.org/doi/10.1109/CGO.2009.21
Ipek EMutlu OMartínez JCaruana R(2008)Self-Optimizing Memory ControllersACM SIGARCH Computer Architecture News10.1145/1394608.138217236:3(39-50)Online publication date: 1-Jun-2008
https://dl.acm.org/doi/10.1145/1394608.1382172
Ipek EMutlu OMartínez JCaruana R(2008)Self-Optimizing Memory ControllersProceedings of the 35th Annual International Symposium on Computer Architecture10.1109/ISCA.2008.21(39-50)Online publication date: 21-Jun-2008
https://dl.acm.org/doi/10.1109/ISCA.2008.21
Stephenson MAmarasinghe S(2005)Predicting Unroll Factors Using Supervised ClassificationProceedings of the international symposium on Code generation and optimization10.1109/CGO.2005.29(123-134)Online publication date: 20-Mar-2005
https://dl.acm.org/doi/10.1109/CGO.2005.29
Cavazos JMoss J(2004)Inducing heuristics to decide whether to scheduleACM SIGPLAN Notices10.1145/996893.99686439:6(183-194)Online publication date: 9-Jun-2004
https://dl.acm.org/doi/10.1145/996893.996864
Cavazos JMoss JPugh WChambers C(2004)Inducing heuristics to decide whether to scheduleProceedings of the ACM SIGPLAN 2004 conference on Programming language design and implementation10.1145/996841.996864(183-194)Online publication date: 9-Jun-2004
https://dl.acm.org/doi/10.1145/996841.996864
Secomandi N(2003)Analysis of a Rollout Approach to Sequencing Problems with Stochastic Routing ApplicationsJournal of Heuristics10.1023/A:10256058034909:4(321-352)Online publication date: 1-Sep-2003
https://dl.acm.org/doi/10.1023/A%3A1025605803490
McGovern AMoss EBarto A(2002)Building a Basic Block Instruction Scheduler with Reinforcement Learning and RolloutsMachine Language10.1023/A:101797621199049:2-3(141-160)Online publication date: 1-Nov-2002
https://dl.acm.org/doi/10.1023/A%3A1017976211990

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Scheduling straight-line code using reinforcement learning and rollouts

Scheduling Straight-Line Code Using Reinforcement Learning and Rollouts

Building a Basic Block Instruction Scheduler with Reinforcement Learning and Rollouts

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