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A data-parallel extension to Ruby for GPGPU: toward a framework for implementing domain-specific optimizations

Authors:

Hidehiko Masuhara,

Yusuke NishiguchiAuthors Info & Claims

RAM-SE '12: Proceedings of the 9th ECOOP Workshop on Reflection, AOP, and Meta-Data for Software Evolution

Pages 3 - 6

https://doi.org/10.1145/2237887.2237888

Published: 13 June 2012 Publication History

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Abstract

We propose Ikra, a data-parallel extension to Ruby for general-purpose computing on graphical processing unit (GPGPU). Our approach is to provide a special array class with higher-order methods for describing computation on a GPU. With a static type inference system that identifies code fragments that shall be executed on a GPU and with a skeleton-based compiler that generates CUDA code, we aim at separating application logic and parallelization and optimizations. The paper presents the design of Ikra and an overview of its implementation along with preliminary performance evaluation.

References

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Chakravarty, M. M., et al. Accelerating Haskell array codes with multicore GPUs. In DAMP, pp. 3--14, 2011.

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[2]

Klöckner, et al. PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation. submitted for publication, Nov. 2009.

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[3]

Lee, S., Min, S.-J., and Eigenmann, R. OpenMP to GPGPU: a compiler framework for automatic translation and optimization. In PPoPP, pp. 101--110, 2009.

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[4]

Nystrom, N., White, D., and Das, K. Firepile: run-time compilation for GPUs in Scala. In GPCE, pp. 107--116, 2011.

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Jacob DTrinder PSinger JMarr SFumero J(2019)Python programmers have GPUs too: automatic Python loop parallelization with staged dependence analysisProceedings of the 15th ACM SIGPLAN International Symposium on Dynamic Languages10.1145/3359619.3359743(42-54)Online publication date: 20-Oct-2019
https://dl.acm.org/doi/10.1145/3359619.3359743
Springer MMasuhara HElsman MGrelck CKlöckner APadua D(2016)Object support in an array-based GPGPU extension for RubyProceedings of the 3rd ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming10.1145/2935323.2935327(25-31)Online publication date: 2-Jun-2016
https://dl.acm.org/doi/10.1145/2935323.2935327
Dhawan UDehon A(2015)Area-Efficient Near-Associative Memories on FPGAsACM Transactions on Reconfigurable Technology and Systems10.1145/26294717:4(1-22)Online publication date: 23-Jan-2015
https://dl.acm.org/doi/10.1145/2629471
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Index Terms

A data-parallel extension to Ruby for GPGPU: toward a framework for implementing domain-specific optimizations
1. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language types
        Concurrent programming languages
        Distributed programming languages
        Parallel programming languages

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Published In

RAM-SE '12: Proceedings of the 9th ECOOP Workshop on Reflection, AOP, and Meta-Data for Software Evolution

June 2012

17 pages

ISBN:9781450312776

DOI:10.1145/2237887

Editors:
Walter Cazzola
Università degli Studi di Milano, Italy
,
Shigeru Chiba
University of Tokyo, Japan
,
Manuel Oriol
University of York, UK
,
Gunter Saake
Otto-von-Guericke-Universität Magdeburg, Germany

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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Association for Computing Machinery

New York, NY, United States

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Published: 13 June 2012

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ECOOP'12: 26th European Conference on Object-Oriented Programming

June 13, 2012

Beijing, China

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Jacob DTrinder PSinger JMarr SFumero J(2019)Python programmers have GPUs too: automatic Python loop parallelization with staged dependence analysisProceedings of the 15th ACM SIGPLAN International Symposium on Dynamic Languages10.1145/3359619.3359743(42-54)Online publication date: 20-Oct-2019
https://dl.acm.org/doi/10.1145/3359619.3359743
Springer MMasuhara HElsman MGrelck CKlöckner APadua D(2016)Object support in an array-based GPGPU extension for RubyProceedings of the 3rd ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming10.1145/2935323.2935327(25-31)Online publication date: 2-Jun-2016
https://dl.acm.org/doi/10.1145/2935323.2935327
Dhawan UDehon A(2015)Area-Efficient Near-Associative Memories on FPGAsACM Transactions on Reconfigurable Technology and Systems10.1145/26294717:4(1-22)Online publication date: 23-Jan-2015
https://dl.acm.org/doi/10.1145/2629471
Bond MKulkarni MCao MZhang MFathi Salmi MBiswas SSengupta AHuang J(2013)OCTETACM SIGPLAN Notices10.1145/2544173.250951948:10(693-712)Online publication date: 29-Oct-2013
https://dl.acm.org/doi/10.1145/2544173.2509519
Noll AGross T(2013)Online feedback-directed optimizations for parallel Java codeACM SIGPLAN Notices10.1145/2544173.250951848:10(713-728)Online publication date: 29-Oct-2013
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Chong NDonaldson AKelly PKetema JQadeer S(2013)Barrier invariantsACM SIGPLAN Notices10.1145/2544173.250951748:10(605-622)Online publication date: 29-Oct-2013
https://dl.acm.org/doi/10.1145/2544173.2509517
Herhut SHudson RShpeisman TSreeram J(2013)River trailACM SIGPLAN Notices10.1145/2544173.250951648:10(729-744)Online publication date: 29-Oct-2013
https://dl.acm.org/doi/10.1145/2544173.2509516
Herhut SHudson RShpeisman TSreeram JHosking AEugster PLopes C(2013)River trailProceedings of the 2013 ACM SIGPLAN international conference on Object oriented programming systems languages & applications10.1145/2509136.2509516(729-744)Online publication date: 29-Oct-2013
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Jaleel ANajaf-abadi HSubramaniam SSteely SEmer J(2012)CRUISEACM SIGARCH Computer Architecture News10.1145/2189750.215100340:1(249-260)Online publication date: 3-Mar-2012
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Feiner PBrown AGoel A(2012)Comprehensive kernel instrumentation via dynamic binary translationACM SIGARCH Computer Architecture News10.1145/2189750.215099240:1(135-146)Online publication date: 3-Mar-2012
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Object support in an array-based GPGPU extension for Ruby

GPGPU: general-purpose computation on graphics hardware

hiCUDA: High-Level GPGPU Programming

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