Article

Towards a Systematic, Pragmatic and Architecture-Aware Program Optimization Process for Complex Processors

Authors:

Jean-Marie VerdunAuthors Info & Claims

SC '04: Proceedings of the 2004 ACM/IEEE conference on Supercomputing

Page 15

https://doi.org/10.1109/SC.2004.61

Published: 06 November 2004 Publication History

Abstract

Because processor architectures are increasingly complex, it is increasingly difficult to embed accurate machine models within compilers. As a result, compiler efficiency tends to decrease. Currently, the trend is on top-down approaches: static compilers are progressively augmented with information from the architecture as in profile-based, iterative or dynamic compilation techniques. However, for the moment, fairly elementary architectural information is used. In this article, we adopt a bottom-up approach to the architecture complexity issue: we assume we know everything about the behavior of the program on the architecture. We present a manual but systematic process for optimizing a program on a complex processor architecture using extensive dynamic analysis, and we find that a small set of run-time information is sufficient to drive anefficient process. We have experimentally observed on an Alpha 21264 that this approach can yield significant performance improvement on Spec benchmarks, beyond peak Spec. We are currently using this approach for optimizing customer applications.

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Cited By

Kœhler TGoens ABhat SGrosser TTrinder PSteuwer M(2024)Guided Equality SaturationProceedings of the ACM on Programming Languages10.1145/36329008:POPL(1727-1758)Online publication date: 5-Jan-2024
https://dl.acm.org/doi/10.1145/3632900
Sohrabizadeh AYu CGao MCong J(2022)AutoDSE: Enabling Software Programmers to Design Efficient FPGA AcceleratorsACM Transactions on Design Automation of Electronic Systems10.1145/349453427:4(1-27)Online publication date: 12-Feb-2022
https://dl.acm.org/doi/10.1145/3494534
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
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Information

Published In

cover image ACM Conferences

SC '04: Proceedings of the 2004 ACM/IEEE conference on Supercomputing

November 2004

724 pages

ISBN:0769521533

General Chair:
Jeff Huskamp

Sponsors

SIGARCH: ACM Special Interest Group on Computer Architecture
IEEE-CS: Computer Society

Publisher

IEEE Computer Society

United States

Publication History

Published: 06 November 2004

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SC '04

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SIGARCH
IEEE-CS

SC '04: International Conference for High Performance Computing, Networking, Storage and Analysis

November 6 - 12, 2004

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SC '04 Paper Acceptance Rate 60 of 200 submissions, 30%;

Overall Acceptance Rate 1,516 of 6,373 submissions, 24%

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Cited By

Kœhler TGoens ABhat SGrosser TTrinder PSteuwer M(2024)Guided Equality SaturationProceedings of the ACM on Programming Languages10.1145/36329008:POPL(1727-1758)Online publication date: 5-Jan-2024
https://dl.acm.org/doi/10.1145/3632900
Sohrabizadeh AYu CGao MCong J(2022)AutoDSE: Enabling Software Programmers to Design Efficient FPGA AcceleratorsACM Transactions on Design Automation of Electronic Systems10.1145/349453427:4(1-27)Online publication date: 12-Feb-2022
https://dl.acm.org/doi/10.1145/3494534
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
Muralidharan SRoy AHall MGarland MRai P(2016)Architecture-Adaptive Code Variant TuningACM SIGARCH Computer Architecture News10.1145/2980024.287241144:2(325-338)Online publication date: 25-Mar-2016
https://dl.acm.org/doi/10.1145/2980024.2872411
Muralidharan SRoy AHall MGarland MRai P(2016)Architecture-Adaptive Code Variant TuningACM SIGPLAN Notices10.1145/2954679.287241151:4(325-338)Online publication date: 25-Mar-2016
https://dl.acm.org/doi/10.1145/2954679.2872411
Muralidharan SRoy AHall MGarland MRai PConte TZhou Y(2016)Architecture-Adaptive Code Variant TuningProceedings of the Twenty-First International Conference on Architectural Support for Programming Languages and Operating Systems10.1145/2872362.2872411(325-338)Online publication date: 25-Mar-2016
https://dl.acm.org/doi/10.1145/2872362.2872411
Park ECavazos JAlvarez MEidt CHoller ASrinivasan UAmarasinghe S(2012)Using graph-based program characterization for predictive modelingProceedings of the Tenth International Symposium on Code Generation and Optimization10.1145/2259016.2259042(196-206)Online publication date: 31-Mar-2012
https://dl.acm.org/doi/10.1145/2259016.2259042
Park EPouchet LCavazos JCohen ASadayappan P(2011)Predictive modeling in a polyhedral optimization spaceProceedings of the 9th Annual IEEE/ACM International Symposium on Code Generation and Optimization10.5555/2190025.2190059(119-129)Online publication date: 2-Apr-2011
https://dl.acm.org/doi/10.5555/2190025.2190059
Namolaru MCohen AFursin GZaks AFreund AKathail VTatge RBarua R(2010)Practical aggregation of semantical program properties for machine learning based optimizationProceedings of the 2010 international conference on Compilers, architectures and synthesis for embedded systems10.1145/1878921.1878951(197-206)Online publication date: 24-Oct-2010
https://dl.acm.org/doi/10.1145/1878921.1878951
Cavazos JFursin GAgakov FBonilla EO'Boyle MTemam O(2007)Rapidly Selecting Good Compiler Optimizations using Performance CountersProceedings of the International Symposium on Code Generation and Optimization10.1109/CGO.2007.32(185-197)Online publication date: 11-Mar-2007
https://dl.acm.org/doi/10.1109/CGO.2007.32
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