abstract

JAWS: a JavaScript framework for adaptive CPU-GPU work sharing

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Jae W. LeeAuthors Info & Claims

PPoPP 2015: Proceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

Pages 251 - 252

https://doi.org/10.1145/2688500.2688525

Published: 24 January 2015 Publication History

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Abstract

This paper introduces jAWS, a JavaScript framework for adaptive work sharing between CPU and GPU for data-parallel workloads. Unlike conventional heterogeneous parallel programming environments for JavaScript, which use only one compute device when executing a single kernel, jAWS accelerates kernel execution by exploiting both devices to realize full performance potential of heterogeneous multicores. jAWS employs an efficient work partitioning algorithm that finds an optimal work distribution between the two devices without requiring offline profiling. The jAWS runtime provides shared arrays for multiple parallel contexts, hence eliminating extra copy overhead for input and output data. Our preliminary evaluation with both CPU-friendly and GPU-friendly benchmarks demonstrates that jAWS provides good load balancing and efficient data communication between parallel contexts, to significantly outperform best single-device execution.

References

[1]

WebCL Standard. URL http://www.khronos.org/webcl/.

Google Scholar

[2]

Web Worker. URL http://www.w3.org/TR/workers.

Google Scholar

[3]

M. Boyer, K. Skadron, S. Che, and N. Jayasena. Load Balancing in a Changing World: Dealing with Heterogeneity and Performance Variability. In CF, 2013.

Digital Library

Google Scholar

[4]

S. Grauer-Gray, L. Xu, R. Searles, S. Ayalasomayajula, and J. Cavazos. Auto-tuning a high-level language targeted to GPU codes. In Proceedings of Innovative Parallel Computing (InPar), 2012.

Crossref

Google Scholar

[5]

P. Pandit and R. Govindarajan. Fluidic Kernels: Cooperative Execution of OpenCL Programs on Multiple Heterogeneous Devices. In CGO, 2014.

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Google Scholar

Cited By

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Park JBaek W(2016)RCHC: A Holistic Runtime System for Concurrent Heterogeneous Computing2016 45th International Conference on Parallel Processing (ICPP)10.1109/ICPP.2016.31(211-216)Online publication date: Aug-2016
https://doi.org/10.1109/ICPP.2016.31
Raju KN Chiplunkar N(2021)PERFORMANCE ENHANCEMENT OF CUDA APPLICATIONS BY OVERLAPPING DATA TRANSFER AND KERNEL EXECUTIONApplied Computer Science10.35784/acs-2021-1717:3(5-18)Online publication date: 30-Sep-2021
https://doi.org/10.35784/acs-2021-17
Patrou MKent KSheppard D(2020)Optimization of JavaScript Large-Scale Urban SimulationsAdvances in Networked-Based Information Systems10.1007/978-3-030-57811-4_3(20-31)Online publication date: 20-Aug-2020
https://doi.org/10.1007/978-3-030-57811-4_3
Show More Cited By

Index Terms

JAWS: a JavaScript framework for adaptive CPU-GPU work sharing
1. Computing methodologies
  1. Concurrent computing methodologies
    1. Concurrent programming languages
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language types
        Concurrent programming languages

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Published In

PPoPP 2015: Proceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

January 2015

290 pages

ISBN:9781450332057

DOI:10.1145/2688500

General Chair:
Albert Cohen
INRIA, France
,
Program Chair:
David Grove
IBM Research, USA

ACM SIGPLAN Notices Volume 50, Issue 8
PPoPP '15
August 2015
290 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/2858788
Editor:
Andy Gill
University of Kansas, Lawrence, KS
Issue’s Table of Contents

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 January 2015

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Funding Sources

Ministry of Science, ICT \& Future Planning (MSIP) under the IT Consilience Creative Program
Ministry of Science, ICT \& Future Planning (MSIP) under the Global Excellent Technology Innovation R&D Program
Ministry of Science, ICT \& Future Planning (MSIP) under the Research Project on High Performance and Scalable Manycore Operating System

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PPoPP '15

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SIGPLAN

PPoPP '15: 20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

February 7 - 11, 2015

CA, San Francisco, USA

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

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Park JBaek W(2016)RCHC: A Holistic Runtime System for Concurrent Heterogeneous Computing2016 45th International Conference on Parallel Processing (ICPP)10.1109/ICPP.2016.31(211-216)Online publication date: Aug-2016
https://doi.org/10.1109/ICPP.2016.31
Raju KN Chiplunkar N(2021)PERFORMANCE ENHANCEMENT OF CUDA APPLICATIONS BY OVERLAPPING DATA TRANSFER AND KERNEL EXECUTIONApplied Computer Science10.35784/acs-2021-1717:3(5-18)Online publication date: 30-Sep-2021
https://doi.org/10.35784/acs-2021-17
Patrou MKent KSheppard D(2020)Optimization of JavaScript Large-Scale Urban SimulationsAdvances in Networked-Based Information Systems10.1007/978-3-030-57811-4_3(20-31)Online publication date: 20-Aug-2020
https://doi.org/10.1007/978-3-030-57811-4_3
K RChiplunkar N(2018)A survey on techniques for cooperative CPU-GPU computingSustainable Computing: Informatics and Systems10.1016/j.suscom.2018.07.01019(72-85)Online publication date: Sep-2018
https://doi.org/10.1016/j.suscom.2018.07.010

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Efficient CPU-GPU work sharing for data-parallel JavaScript workloads