research-article

Automatic compilation of MATLAB programs for synergistic execution on heterogeneous processors

Authors:

Jayvant Anantpur,

R. GovindarajanAuthors Info & Claims

ACM SIGPLAN Notices, Volume 46, Issue 6

Pages 152 - 163

https://doi.org/10.1145/1993316.1993517

Published: 04 June 2011 Publication History

Abstract

MATLAB is an array language, initially popular for rapid prototyping, but is now being increasingly used to develop production code for numerical and scientific applications. Typical MATLAB programs have abundant data parallelism. These programs also have control flow dominated scalar regions that have an impact on the program's execution time. Today's computer systems have tremendous computing power in the form of traditional CPU cores and throughput oriented accelerators such as graphics processing units(GPUs). Thus, an approach that maps the control flow dominated regions to the CPU and the data parallel regions to the GPU can significantly improve program performance.

In this paper, we present the design and implementation of MEGHA, a compiler that automatically compiles MATLAB programs to enable synergistic execution on heterogeneous processors. Our solution is fully automated and does not require programmer input for identifying data parallel regions. We propose a set of compiler optimizations tailored for MATLAB. Our compiler identifies data parallel regions of the program and composes them into kernels. The problem of combining statements into kernels is formulated as a constrained graph clustering problem. Heuristics are presented to map identified kernels to either the CPU or GPU so that kernel execution on the CPU and the GPU happens synergistically and the amount of data transfer needed is minimized. In order to ensure required data movement for dependencies across basic blocks, we propose a data flow analysis and edge splitting strategy. Thus our compiler automatically handles composition of kernels, mapping of kernels to CPU and GPU, scheduling and insertion of required data transfer. The proposed compiler was implemented and experimental evaluation using a set of MATLAB benchmarks shows that our approach achieves a geometric mean speedup of 19.8X for data parallel benchmarks over native execution of MATLAB.

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Cited By

Reis LBispo JCardoso J(2020)Compilation of MATLAB computations to CPU/GPU via C/OpenCL generationConcurrency and Computation: Practice and Experience10.1002/cpe.585432:22Online publication date: Jul-2020
https://doi.org/10.1002/cpe.5854
Celik ANie PRossbach CGligoric M(2019)Design, implementation, and application of GPU-based Java bytecode interpretersProceedings of the ACM on Programming Languages10.1145/33606033:OOPSLA(1-28)Online publication date: 10-Oct-2019
https://dl.acm.org/doi/10.1145/3360603
Reis LBispo JCardoso JMcIntosh-Smith SBergen B(2017)Compiler Techniques for Efficient MATLAB to OpenCL Code GenerationProceedings of the 5th International Workshop on OpenCL10.1145/3078155.3078186(1-2)Online publication date: 16-May-2017
https://dl.acm.org/doi/10.1145/3078155.3078186
Show More Cited By

Index Terms

Automatic compilation of MATLAB programs for synergistic execution on heterogeneous processors
1. Software and its engineering
  1. Software notations and tools
    1. Compilers
    2. General programming languages
      1. Language types
        Very high level languages

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Published In

cover image ACM SIGPLAN Notices

ACM SIGPLAN Notices Volume 46, Issue 6

PLDI '11

June 2011

652 pages

ISSN:0362-1340

EISSN:1558-1160

DOI:10.1145/1993316

Issue’s Table of Contents

PLDI '11: Proceedings of the 32nd ACM SIGPLAN Conference on Programming Language Design and Implementation
June 2011
668 pages
ISBN:9781450306638
DOI:10.1145/1993498
General Chair:
Mary Hall
University of Utah
,
Program Chair:
David Padua
University of Illinois at Urbana-Champaign

Copyright © 2011 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 June 2011

Published in SIGPLAN Volume 46, Issue 6

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Cited By

Reis LBispo JCardoso J(2020)Compilation of MATLAB computations to CPU/GPU via C/OpenCL generationConcurrency and Computation: Practice and Experience10.1002/cpe.585432:22Online publication date: Jul-2020
https://doi.org/10.1002/cpe.5854
Celik ANie PRossbach CGligoric M(2019)Design, implementation, and application of GPU-based Java bytecode interpretersProceedings of the ACM on Programming Languages10.1145/33606033:OOPSLA(1-28)Online publication date: 10-Oct-2019
https://dl.acm.org/doi/10.1145/3360603
Reis LBispo JCardoso JMcIntosh-Smith SBergen B(2017)Compiler Techniques for Efficient MATLAB to OpenCL Code GenerationProceedings of the 5th International Workshop on OpenCL10.1145/3078155.3078186(1-2)Online publication date: 16-May-2017
https://dl.acm.org/doi/10.1145/3078155.3078186
Collins TWyglinski A(2017)Dataflow in MATLAB: Algorithm Acceleration Through ConcurrencyIEEE Access10.1109/ACCESS.2017.26722005(2308-2318)Online publication date: 2017
https://doi.org/10.1109/ACCESS.2017.2672200
Bispo JCardoso J(2017)A MATLAB subset to C compiler targeting embedded systemsSoftware—Practice & Experience10.1002/spe.240847:2(249-272)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1002/spe.2408
Reis LBispo JCardoso JElsman MGrelck CKlöckner APadua D(2016)SSA-based MATLAB-to-C compilation and optimizationProceedings of the 3rd ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming10.1145/2935323.2935330(55-62)Online publication date: 2-Jun-2016
https://dl.acm.org/doi/10.1145/2935323.2935330
Elsman MGrelck CKlöckner APadua D(2016)Proceedings of the 3rd ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array ProgrammingundefinedOnline publication date: 2-Jun-2016
Mittal SVetter J(2015)A Survey of CPU-GPU Heterogeneous Computing TechniquesACM Computing Surveys10.1145/278839647:4(1-35)Online publication date: 21-Jul-2015
https://dl.acm.org/doi/10.1145/2788396
Bispo JReis LCardoso JHendren LMasuhara HSheeran MVitek J(2015)Techniques for efficient MATLAB-to-C compilationProceedings of the 2nd ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming10.1145/2774959.2774961(7-12)Online publication date: 13-Jun-2015
https://dl.acm.org/doi/10.1145/2774959.2774961
Bispo JReis LCardoso JWainwright RCorchado JBechini AHong J(2015)C and OpenCL generation from MATLABProceedings of the 30th Annual ACM Symposium on Applied Computing10.1145/2695664.2695911(1315-1320)Online publication date: 13-Apr-2015
https://dl.acm.org/doi/10.1145/2695664.2695911
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