tutorial

A Composable Array Function Interface for Heterogeneous Computing in Java

Authors:

Juan José Fumero,

Michel Steuwer,

Christophe DubachAuthors Info & Claims

ARRAY'14: Proceedings of ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming

Pages 44 - 49

https://doi.org/10.1145/2627373.2627381

Published: 09 June 2014 Publication History

Abstract

Heterogeneous computing has now become mainstream with virtually every desktop machines featuring accelerators such as Graphics Processing Units (GPUs). While heterogeneity offers the promise of high-performance and high-efficiency, it comes at the cost of huge programming difficulties. Languages and interfaces for programming such system tend to be low-level and require expert knowledge of the hardware in order to achieve its potential.

A promising approach for programming such heterogeneous systems is the use of array programming. This style of programming relies on well known parallel patterns that can be easily translated into GPU or other accelerator code. However, only little work has been done on integrating such concepts in mainstream languages such as Java.

In this work, we propose a new Array Function interface implemented with the new features from Java 8. While similar in spirit to the new Stream API of Java, our API follows a different design based on reusability and composability. We demonstrate that this API can be used to generate OpenCL code for a simple application. We present encouraging preliminary performance results showing the potential of our approach.

References

[1]

Aparapi. https://code.google.com/p/aparapi/.

[2]

Project Sumatra. http://openjdk.java.net/projects/sumatra/.

[3]

B. Catanzaro, M. Garland, and K. Keutzer. Copperhead: Compiling an embedded data parallel language. In Proceedings of the 16th ACM Symposium on Principles and Practice of Parallel Programming, PPoPP '11, pages 47--56, New York, NY, USA, 2011. ACM.

Digital Library

[4]

M. M. T. Chakravarty, G. Keller, S. Lee, T. L. McDonell, and V. Grover. Accelerating haskell array codes with multicore gpus. In Proceedings of the POPL 2011 Workshop on Declarative Aspects of Multicore Programming, pages 3--14, 2011.

Digital Library

[5]

A. Collins, D. Grewe, V. Grover, S. Lee, and A. Susnea. NOVA: A functional language for data parallelism. Nvidia, 2013. Technical Report.

[6]

CUDA. Nvidia CUDA. http://developer.nvidia.com/.

[7]

C. Dubach, P. Cheng, R. Rabbah, D. F. Bacon, and S. J. Fink. Compiling a high-level language for gpus: (via language support for architectures and compilers). In Proceedings of the 33rd ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI '12, pages 1--12, New York, NY, USA, 2012. ACM.

Digital Library

[8]

G. Duboscq, T. Würthinger, L. Stadler, C. Wimmer, D. Simon, and H. Mössenböck. Graal ir: An intermediate representation for speculative optimizations in a dynamic compiler. In Proceedings of the 7th ACM Workshop on Virtual Machines and Intermediate Languages, VMIL '13, pages 1--10, New York, NY, USA, 2013. ACM.

Digital Library

[9]

J. Guo, W. Rodrigues, J. Thiyagalingam, F. Guyomarc'h, P. Boulet, and S.-B. Scholz. Harnessing the power of GPUs without losing abstractions in SAC and ArrayOL: A comparative study. In Proceedings of the IPDPS 2011 Workshop on High-Level Parallel Programming Models and Supportive Environments, pages 1183--1190, 2011.

Digital Library

[10]

S. Herhut, R. L. Hudson, T. Shpeisman, and J. Sreeram. River trail: A path to parallelism in javascript. In Proceedings of the 2013 ACM SIGPLAN International Conference on Object Oriented Programming Systems Languages & Applications, OOPSLA '13, pages 729--744, New York, NY, USA, 2013. ACM. ISBN 978-1-4503-2374-1. URL http://doi.acm.org/10.1145/2509136.2509516.

Digital Library

[11]

J. Hoberock and N. Bell. Thrust: A parallel template library. http://developer.nvidia.com/thrust.

[12]

JOCL. Java bindings for OpenCL. http://www.jocl.org/.

[13]

R. Lämmel. Google's MapReduce programming model -- revisited. Science of Computer Programming, 70(1):1--30, 2008.

Digital Library

[14]

D. Lea. The java.util.concurrent synchronizer framework. Sci. Comput. Program., 58(3):293--309, Dec. 2005. ISSN 0167-6423. URL http://dx.doi.org/10.1016/j.scico.2005.03.007.

Digital Library

[15]

M. McCool, A. D. Robison, and J. Reinders. Structured Parallel Programming. Morgan Kaufmann, 2012.

Digital Library

[16]

F. Mueller and Y. Zhang. Hidp: A hierarchical data parallel language. In Proceedings of the 2013 IEEE/ACM International Symposium on Code Generation and Optimization (CGO), CGO '13, pages 1--11, Washington, DC, USA, 2013. IEEE Computer Society.

Digital Library

[17]

OpenACC. OpenACC. http://www.openacc-standard.org/.

[18]

OpenCL. Opencl. http://www.khronos.org/opencl/.

[19]

P. C. Pratt-Szeliga, J. W. Fawcett, and R. D. Welch. Rootbeer: Seamlessly using GPUs from java. In G. Min, J. Hu, L. C. Liu, L. T. Yang, S. Seelam, and L. Lefevre, editors, HPCC-ICESS, 2012.

Digital Library

[20]

M. Steuwer and S. Gorlatch. SkelCL: Enhancing OpenCL for high-level programming of multi-GPU systems. In Parallel Computing Technologies, volume 7979 of Lecture Notes in Computer Science, pages 258--272. Springer, 2013.

Digital Library

[21]

W. Thies and S. Amarasinghe. An empirical characterization of stream programs and its implications for language and compiler design. In Proceedings of the 19th international conference on Parallel architectures and compilation techniques, PACT '10, pages 365--376, New York, NY, USA, 2010. ACM.

Digital Library

Cited By

Fumero JKotselidis CKell SMarr S(2018)Using compiler snippets to exploit parallelism on heterogeneous hardware: a Java reduction case studyProceedings of the 10th ACM SIGPLAN International Workshop on Virtual Machines and Intermediate Languages10.1145/3281287.3281292(16-25)Online publication date: 4-Nov-2018
https://dl.acm.org/doi/10.1145/3281287.3281292
Clarkson JFumero JPapadimitriou MZakkak FXekalaki MKotselidis CLuján MTilevich EMössenböck H(2018)Exploiting high-performance heterogeneous hardware for Java programs using graalProceedings of the 15th International Conference on Managed Languages & Runtimes10.1145/3237009.3237016(1-13)Online publication date: 12-Sep-2018
https://dl.acm.org/doi/10.1145/3237009.3237016
Clarkson JFumero JPapadimitriou MXekalaki MKotselidis CMarr SSartor J(2018)Towards practical heterogeneous virtual machinesCompanion Proceedings of the 2nd International Conference on the Art, Science, and Engineering of Programming10.1145/3191697.3191730(46-48)Online publication date: 9-Apr-2018
https://dl.acm.org/doi/10.1145/3191697.3191730
Show More Cited By

Index Terms

A Composable Array Function Interface for Heterogeneous Computing in Java
1. Software and its engineering
  1. Software notations and tools
    1. Compilers
    2. General programming languages
      1. Language features

Recommendations

Runtime Code Generation and Data Management for Heterogeneous Computing in Java
PPPJ '15: Proceedings of the Principles and Practices of Programming on The Java Platform

GPUs (Graphics Processing Unit) and other accelerators are nowadays commonly found in desktop machines, mobile devices and even data centres. While these highly parallel processors offer high raw performance, they also dramatically increase program ...
The tradeoffs of fused memory hierarchies in heterogeneous computing architectures
CF '12: Proceedings of the 9th conference on Computing Frontiers

With the rise of general purpose computing on graphics processing units (GPGPU), the influence from consumer markets can now be seen across the spectrum of computer architectures. In fact, many of the high-ranking Top500 HPC systems now include these ...
On the Efficacy of a Fused CPU+GPU Processor (or APU) for Parallel Computing
SAAHPC '11: Proceedings of the 2011 Symposium on Application Accelerators in High-Performance Computing

The graphics processing unit (GPU) has made significant strides as an accelerator in parallel computing. However, because the GPU has resided out on PCIe as a discrete device, the performance of GPU applications can be bottlenecked by data transfers ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

ARRAY'14: Proceedings of ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming

June 2014

112 pages

ISBN:9781450329378

DOI:10.1145/2627373

Copyright © 2014 ACM.

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 the author(s) 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].

Sponsors

SIGPLAN: ACM Special Interest Group on Programming Languages

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 June 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Tutorial
Research
Refereed limited

Conference

PLDI '14

Sponsor:

SIGPLAN

PLDI '14: ACM SIGPLAN Conference on Programming Language Design and Implementation

June 9 - 11, 2014

Edinburgh, United Kingdom

Acceptance Rates

ARRAY'14 Paper Acceptance Rate 17 of 25 submissions, 68%;

Overall Acceptance Rate 17 of 25 submissions, 68%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

12
Total Citations
View Citations
216
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 18 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Fumero JKotselidis CKell SMarr S(2018)Using compiler snippets to exploit parallelism on heterogeneous hardware: a Java reduction case studyProceedings of the 10th ACM SIGPLAN International Workshop on Virtual Machines and Intermediate Languages10.1145/3281287.3281292(16-25)Online publication date: 4-Nov-2018
https://dl.acm.org/doi/10.1145/3281287.3281292
Clarkson JFumero JPapadimitriou MZakkak FXekalaki MKotselidis CLuján MTilevich EMössenböck H(2018)Exploiting high-performance heterogeneous hardware for Java programs using graalProceedings of the 15th International Conference on Managed Languages & Runtimes10.1145/3237009.3237016(1-13)Online publication date: 12-Sep-2018
https://dl.acm.org/doi/10.1145/3237009.3237016
Clarkson JFumero JPapadimitriou MXekalaki MKotselidis CMarr SSartor J(2018)Towards practical heterogeneous virtual machinesCompanion Proceedings of the 2nd International Conference on the Art, Science, and Engineering of Programming10.1145/3191697.3191730(46-48)Online publication date: 9-Apr-2018
https://dl.acm.org/doi/10.1145/3191697.3191730
Xekalaki MFumero JKotselidis C(2018)Challenges and Proposals for Enabling Dynamic Heterogeneous Execution of Big Data Frameworks2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)10.1109/CloudCom2018.2018.00070(335-341)Online publication date: Dec-2018
https://doi.org/10.1109/CloudCom2018.2018.00070
Kotselidis CClarkson JRodchenko ANisbet AMawer JLuján M(2017)Heterogeneous Managed Runtime SystemsACM SIGPLAN Notices10.1145/3140607.305076452:7(74-82)Online publication date: 8-Apr-2017
https://dl.acm.org/doi/10.1145/3140607.3050764
Fumero JSteuwer MStadler LDubach C(2017)Just-In-Time GPU Compilation for Interpreted Languages with Partial EvaluationACM SIGPLAN Notices10.1145/3140607.305076152:7(60-73)Online publication date: 8-Apr-2017
https://dl.acm.org/doi/10.1145/3140607.3050761
Kotselidis CClarkson JRodchenko ANisbet AMawer JLuján M(2017)Heterogeneous Managed Runtime SystemsProceedings of the 13th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments10.1145/3050748.3050764(74-82)Online publication date: 8-Apr-2017
https://dl.acm.org/doi/10.1145/3050748.3050764
Fumero JSteuwer MStadler LDubach C(2017)Just-In-Time GPU Compilation for Interpreted Languages with Partial EvaluationProceedings of the 13th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments10.1145/3050748.3050761(60-73)Online publication date: 8-Apr-2017
https://dl.acm.org/doi/10.1145/3050748.3050761
Clarkson JKotselidis CBrown GLuján M(2017)Boosting Java Performance Using GPGPUsArchitecture of Computing Systems - ARCS 201710.1007/978-3-319-54999-6_5(59-70)Online publication date: 4-Mar-2017
https://doi.org/10.1007/978-3-319-54999-6_5
Hayashi AIshizaki KKoblents GSarkar VStansifer RKrall A(2015)Machine-Learning-based Performance Heuristics for Runtime CPU/GPU SelectionProceedings of the Principles and Practices of Programming on The Java Platform10.1145/2807426.2807429(27-36)Online publication date: 8-Sep-2015
https://dl.acm.org/doi/10.1145/2807426.2807429
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents