Article

ACME: adaptive compilation made efficient

Authors:

Keith D. Cooper,

Alexander Grosul,

Timothy J. Harvey,

Devika Subramanian,

Todd WatermanAuthors Info & Claims

LCTES '05: Proceedings of the 2005 ACM SIGPLAN/SIGBED conference on Languages, compilers, and tools for embedded systems

Pages 69 - 77

https://doi.org/10.1145/1065910.1065921

Published: 15 June 2005 Publication History

Abstract

Research over the past five years has shown significant performance improvements using a technique called adaptive compilation. An adaptive compiler uses a compile-execute-analyze feedback loop to find the combination of optimizations and parameters that minimizes some performance goal, such as code size or execution time.Despite its ability to improve performance, adaptive compilation has not seen widespread use because of two obstacles: the large amounts of time that such systems have used to perform the many compilations and executions prohibits most users from adopting these systems, and the complexity inherent in a feedback-driven adaptive system has made it difficult to build and hard to use.A significant portion of the adaptive compilation process is devoted to multiple executions of the code being compiled. We have developed a technique called virtual execution to address this problem. Virtual execution runs the program a single time and preserves information that allows us to accurately predict the performance of different optimization sequences without running the code again. Our prototype implementation of this technique significantly reduces the time required by our adaptive compiler.In conjunction with this performance boost, we have developed a graphical-user interface (GUI) that provides a controlled view of the compilation process. By providing appropriate defaults, the interface limits the amount of information that the user must provide to get started. At the same time, it lets the experienced user exert fine-grained control over the parameters that control the system.

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Cited By

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
Maramzin AVasiladiotis CLozano RCole MFranke BAtoofian ETakizawa H(2019)“It looks like you’re writing a parallel loop”: a machine learning based parallelization assistantProceedings of the 6th ACM SIGPLAN International Workshop on AI-Inspired and Empirical Methods for Software Engineering on Parallel Computing Systems10.1145/3358500.3361567(1-10)Online publication date: 22-Oct-2019
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Lattuada MFerrandi F(2019)A Design Flow Engine for the Support of Customized Dynamic High Level Synthesis FlowsACM Transactions on Reconfigurable Technology and Systems10.1145/335647512:4(1-26)Online publication date: 31-Oct-2019
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Index Terms

ACME: adaptive compilation made efficient
1. Software and its engineering
  1. Software notations and tools
    1. Compilers

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Published In

cover image ACM Conferences

LCTES '05: Proceedings of the 2005 ACM SIGPLAN/SIGBED conference on Languages, compilers, and tools for embedded systems

June 2005

248 pages

ISBN:1595930183

DOI:10.1145/1065910

General Chair:
Yunheung Paek
Seoul National University, Seoul, Korea
,
Program Chair:
Rajiv Gupta
University of Arizona, Tucson, USA

ACM SIGPLAN Notices Volume 40, Issue 7
Proceedings of the 2005 ACM SIGPLAN/SIGBED conference on Languages, compilers, and tools for embedded systems
July 2005
238 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/1070891
Issue’s Table of Contents

Copyright © 2005 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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Publication History

Published: 15 June 2005

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adaptive compilation

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LCTES05

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LCTES05: Languages, Compilers, and Tools for Embedded Systems 2005

June 15 - 17, 2005

Illinois, Chicago, USA

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Overall Acceptance Rate 116 of 438 submissions, 26%

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Cited By

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
Maramzin AVasiladiotis CLozano RCole MFranke BAtoofian ETakizawa H(2019)“It looks like you’re writing a parallel loop”: a machine learning based parallelization assistantProceedings of the 6th ACM SIGPLAN International Workshop on AI-Inspired and Empirical Methods for Software Engineering on Parallel Computing Systems10.1145/3358500.3361567(1-10)Online publication date: 22-Oct-2019
https://dl.acm.org/doi/10.1145/3358500.3361567
Lattuada MFerrandi F(2019)A Design Flow Engine for the Support of Customized Dynamic High Level Synthesis FlowsACM Transactions on Reconfigurable Technology and Systems10.1145/335647512:4(1-26)Online publication date: 31-Oct-2019
https://dl.acm.org/doi/10.1145/3356475
Dardaillon MSkalistis SPuaut IDerrien S(2019)Reconciling Compiler Optimizations and WCET Estimation Using Iterative Compilation2019 IEEE Real-Time Systems Symposium (RTSS)10.1109/RTSS46320.2019.00022(133-145)Online publication date: Dec-2019
https://doi.org/10.1109/RTSS46320.2019.00022
Memeti SPllana SBinotto AKołodziej JBrandic I(2019)Using meta-heuristics and machine learning for software optimization of parallel computing systemsComputing10.1007/s00607-018-0614-9101:8(893-936)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s00607-018-0614-9
Ganser SGrößlinger ASiegmund NApel SLengauer C(2018)Speeding up Iterative Polyhedral Schedule Optimization with Surrogate Performance ModelsACM Transactions on Architecture and Code Optimization10.1145/329177315:4(1-27)Online publication date: 19-Dec-2018
https://dl.acm.org/doi/10.1145/3291773
Memeti SPllana SBinotto AKołodziej JBrandic I(2018)A Review of Machine Learning and Meta-heuristic Methods for Scheduling Parallel Computing SystemsProceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications10.1145/3230905.3230906(1-6)Online publication date: 2-May-2018
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Wang ZO'Boyle M(2018)Machine Learning in Compiler OptimizationProceedings of the IEEE10.1109/JPROC.2018.2817118106:11(1879-1901)Online publication date: Nov-2018
https://doi.org/10.1109/JPROC.2018.2817118
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
Popov MAkel CChatelain YJalby Wde Oliveira Castro P(2017)Piecewise holistic autotuning of parallel programs with CEREConcurrency and Computation: Practice and Experience10.1002/cpe.419029:15Online publication date: 20-Jun-2017
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