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Optimizing GPU programs by partial evaluation

Authors:

Aleksey Tyurin,

Daniil Berezun,

Semyon GrigorevAuthors Info & Claims

PPoPP '20: Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

Pages 431 - 432

https://doi.org/10.1145/3332466.3374507

Published: 19 February 2020 Publication History

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Abstract

While GPU utilization allows one to speed up computations to the orders of magnitude, memory management remains the bottleneck making it often a challenge to achieve the desired performance. Hence, different memory optimizations are leveraged to make memory being used more effectively. We propose an approach automating memory management utilizing partial evaluation, a program transformation technique that enables data accesses to be pre-computed, optimized, and embedded into the code, saving memory transactions. An empirical evaluation of our approach shows that the transformed program could be up to 8 times as efficient as the original one in the case of CUDA C naïve string pattern matching algorithm implementation.

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Keir PGozillon A(2024)ClangOz: Parallel constant evaluation of C++ map and reduce operationsJournal of Computer Languages10.1016/j.cola.2024.10129881(101298)Online publication date: Nov-2024
https://doi.org/10.1016/j.cola.2024.101298
Gozillon AHaeri SRiordan JKeir P(2024)Performance Analysis of Compiler Support for Parallel Evaluation of C++ Constant ExpressionsSoftware, System, and Service Engineering10.1007/978-3-031-51075-5_6(129-152)Online publication date: 3-Jan-2024
https://doi.org/10.1007/978-3-031-51075-5_6
Адамович И(2021)The JaSpe specializer: BT-objects and the interprocedural aspect of the binding-time analysis algorithmProgram Systems: Theory and ApplicationsПрограммные системы: теория и приложения10.25209/2079-3316-2021-12-4-3-3212:4(3-32)Online publication date: 2021
https://doi.org/10.25209/2079-3316-2021-12-4-3-32

Index Terms

Optimizing GPU programs by partial evaluation
1. Software and its engineering
  1. Software notations and tools
    1. Compilers
      1. Source code generation
    2. General programming languages

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PPoPP '20: Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

February 2020

454 pages

ISBN:9781450368186

DOI:10.1145/3332466

General Chair:
Rajiv Gupta
UC Riverside
,
Program Chair:
Xipeng Shen
NCSU

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 19 February 2020

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PPoPP '20: 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

February 22 - 26, 2020

California, San Diego

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PPoPP '20 Paper Acceptance Rate 28 of 121 submissions, 23%;

Overall Acceptance Rate 230 of 1,014 submissions, 23%

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Keir PGozillon A(2024)ClangOz: Parallel constant evaluation of C++ map and reduce operationsJournal of Computer Languages10.1016/j.cola.2024.10129881(101298)Online publication date: Nov-2024
https://doi.org/10.1016/j.cola.2024.101298
Gozillon AHaeri SRiordan JKeir P(2024)Performance Analysis of Compiler Support for Parallel Evaluation of C++ Constant ExpressionsSoftware, System, and Service Engineering10.1007/978-3-031-51075-5_6(129-152)Online publication date: 3-Jan-2024
https://doi.org/10.1007/978-3-031-51075-5_6
Адамович И(2021)The JaSpe specializer: BT-objects and the interprocedural aspect of the binding-time analysis algorithmProgram Systems: Theory and ApplicationsПрограммные системы: теория и приложения10.25209/2079-3316-2021-12-4-3-3212:4(3-32)Online publication date: 2021
https://doi.org/10.25209/2079-3316-2021-12-4-3-32

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Architecture-Aware Mapping and Optimization on a 1600-Core GPU

SIMD Monte-Carlo Numerical Simulations Accelerated on GPU and Xeon Phi

Pervasive massively multithreaded GPU processors

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Architecture-Aware Mapping and Optimization on a 1600-Core GPU

SIMD Monte-Carlo Numerical Simulations Accelerated on GPU and Xeon Phi

Pervasive massively multithreaded GPU processors

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