Article

Finding effective optimization phase sequences

Authors:

Prasad Kulkarni,

Kyle GallivanAuthors Info & Claims

LCTES '03: Proceedings of the 2003 ACM SIGPLAN conference on Language, compiler, and tool for embedded systems

Pages 12 - 23

https://doi.org/10.1145/780732.780735

Published: 11 June 2003 Publication History

Abstract

It has long been known that a single ordering of optimization phases will not produce the best code for every application. This phase ordering problem can be more severe when generating code for embedded systems due to the need to meet conflicting constraints on time, code size, and power consumption. Given that many embedded application developers are willing to spend time tuning an application, we believe a viable approach is to allow the developer to steer the process of optimizing a function. In this paper, we describe support in VISTA, an interactive compilation system, for finding effective sequences of optimization phases. VISTA provides the user with dynamic and static performance information that can be used during an interactive compilation session to gauge the progress of improving the code. In addition, VISTA provides support for automatically using performance information to select the best optimization sequence among several attempted. One such feature is the use of a genetic algorithm to search for the most efficient sequence based on specified fitness criteria. We have included a number of experimental results that evaluate the effectiveness of using a genetic algorithm in VISTA to find effective optimization phase sequences.

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Cited By

Gao FChen HZhou YWang KFilkov VRay BZhou M(2024)Shoot Yourself in the Foot — Efficient Code Causes Inefficiency in Compiler OptimizationsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695548(1846-1857)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695548
da Silva AKind Bde Souza Magalhães JRocha JGuimarães BPereira FLee J(2021)AnghaBenchProceedings of the 2021 IEEE/ACM International Symposium on Code Generation and Optimization10.1109/CGO51591.2021.9370322(378-390)Online publication date: 27-Feb-2021
https://dl.acm.org/doi/10.1109/CGO51591.2021.9370322
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
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Index Terms

Finding effective optimization phase sequences
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems
2. Software and its engineering
  1. Software notations and tools
    1. Compilers
  2. Software organization and properties
    1. Software system structures
      1. Embedded software
      2. Real-time systems software

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Finding effective optimization phase sequences
Special Issue: Proceedings of the 2003 ACM SIGPLAN conference on Language, compiler, and tool support for embedded systems (San Diego, CA).

It has long been known that a single ordering of optimization phases will not produce the best code for every application. This phase ordering problem can be more severe when generating code for embedded systems due to the need to meet conflicting ...
VISTA: VPO interactive system for tuning applications

Software designers face many challenges when developing applications for embedded systems. One major challenge is meeting the conflicting constraints of speed, code size, and power consumption. Embedded application developers often resort to hand-coded ...
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It has long been known that a fixed ordering of optimization phases will not produce the best code for every application. One approach for addressing this phase-ordering problem is to use an evolutionary algorithm to search for a specific sequence of ...

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Published In

cover image ACM Conferences

LCTES '03: Proceedings of the 2003 ACM SIGPLAN conference on Language, compiler, and tool for embedded systems

June 2003

304 pages

ISBN:1581136471

DOI:10.1145/780732

General Chair:
Frank Mueller
North Carolina State University
,
Publications Chair:
Uli Kremer
Rutgers University

ACM SIGPLAN Notices Volume 38, Issue 7
Special Issue: Proceedings of the 2003 ACM SIGPLAN conference on Language, compiler, and tool support for embedded systems (San Diego, CA).
July 2003
293 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/780731
Issue’s Table of Contents

Copyright © 2003 ACM.

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Published: 11 June 2003

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LCTES03: ACM SIGPLAN Symposium on Languages, Compilers, and Tools for Embedded Systems

June 11 - 13, 2003

California, San Diego, USA

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LCTES '03 Paper Acceptance Rate 29 of 128 submissions, 23%;

Overall Acceptance Rate 116 of 438 submissions, 26%

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Cited By

Gao FChen HZhou YWang KFilkov VRay BZhou M(2024)Shoot Yourself in the Foot — Efficient Code Causes Inefficiency in Compiler OptimizationsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695548(1846-1857)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695548
da Silva AKind Bde Souza Magalhães JRocha JGuimarães BPereira FLee J(2021)AnghaBenchProceedings of the 2021 IEEE/ACM International Symposium on Code Generation and Optimization10.1109/CGO51591.2021.9370322(378-390)Online publication date: 27-Feb-2021
https://dl.acm.org/doi/10.1109/CGO51591.2021.9370322
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
Shen DChabbi MLiu X(2018)An Evaluation of Vectorization and Cache Reuse Tradeoffs on Modern CPUsProceedings of the 9th International Workshop on Programming Models and Applications for Multicores and Manycores10.1145/3178442.3178445(21-30)Online publication date: 24-Feb-2018
https://dl.acm.org/doi/10.1145/3178442.3178445
Garciarena USantana RNeumann FSutton A(2016)Evolutionary Optimization of Compiler Flag Selection by Learning and Exploiting Flags InteractionsProceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion10.1145/2908961.2931696(1159-1166)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908961.2931696
Chebolu NWankar R(2016)Tuning compilations landscape2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)10.1109/IC3I.2016.7918029(575-583)Online publication date: Dec-2016
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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
Sher GMartin KDechev DBrandner FVander Aa T(2014)Preliminary results for neuroevolutionary optimization phase order generation for static compilationProceedings of the 11th Workshop on Optimizations for DSP and Embedded Systems10.1145/2568326.2568328(33-40)Online publication date: 15-Feb-2014
https://dl.acm.org/doi/10.1145/2568326.2568328
Yi QWang QCui HFlautner KWenisch TOzer EFerdman M(2014)Specializing Compiler Optimizations through Programmable Composition for Dense Matrix ComputationsProceedings of the 47th Annual IEEE/ACM International Symposium on Microarchitecture10.1109/MICRO.2014.14(596-608)Online publication date: 13-Dec-2014
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Li FTang FShen Y(2014)Feature Mining for Machine Learning Based Compilation OptimizationProceedings of the 2014 Eighth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing10.1109/IMIS.2014.26(207-214)Online publication date: 2-Jul-2014
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