skip to main content
10.1145/3206098.3206104acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicisdmConference Proceedingsconference-collections
research-article

An Architecture for Translating Sequential Code to Parallel

Published: 09 April 2018 Publication History

Abstract

With the increasing progress of high performance computing infrastructure, it has become necessary to design and implement easy to use tools that are able to translate any legacy software application to parallel. A deep observation of the existing tools allowing such translation or conversion reveals that all of them are still far away from their expectations. Indeed, some recent studies stress the urgent need for such tools in order to respond better to users' needs in term of performance. In this paper, we propose a novel architecture based on web services which is able to translate any legacy software application into a parallel code. The resulting parallel code can be generated for any parallel programming model, also called the parallelization technique, such as MPI, OpenMP, CUDA, OpenCL or hybrid model. Our ultimate objective through this research is twofold. The first one is providing users a flexible architecture which is able to translate any sequential code into a parallel one, whatever the target parallel programming model. The second objective is providing parallel application developers a wide range of useful, easy to use parallel codes built as web services which can be included and used in their applications.

References

[1]
M. A. S. Netto, R N. Calheiros, E. Rodrigues, R. L. F. Cunha, R. Buyy, A HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges, ACM Computing Surveys, Vol. 1, No. 1, October 2017.
[2]
Sudhakar Sah qnd Vinay G. Vaidya, A Review of Parallelization Tools and Introduction to Easypar, International Journal of Computer Applications, Volume 56-- No.12, October 2012, 17--29.
[3]
Xiaohui Chen, MetaFork: A Compilation Framework for Concurrency Models Targeting Hardware Accelerators. PhD Thesis, University of Western Ontario, April 2017.
[4]
Anne Meade, Deva Kumar Deeptimahanti, Jim Buckley and J. J. Collins, An empirical study of data decomposition for software parallelization, Journal of Systems and Software, Volume 125, March 2017, Pages 401--416.
[5]
Manju Mathews and Jisha P Abraham, Automatic Code Parallelization with OpenMP task constructs, 2016 International Conference in Information Science (ICIS), 233--238.
[6]
Alcides Fonseca and Bruno Cabral, Controlling the granularity of automatic parallel programs, Journal of Computational Science, Volume 17, Part 3, November 2016, Pages 620--629.
[7]
Adam D. Barwell, Christopher Brown, and Kevin Hammond, "Finding parallel functional pearls: Automatic parallel recursion scheme detection in Haskell functions via anti-unification, Future Generation Computer Systems, Available online 27 July 201.
[8]
Aditi Athavale, Priti Ranadive, M. N. Babu, Prasad Pawar, Sudhakar Sah., Vinay Vaidya., Chaitanya Rajguru, Automatic Sequential to Parallel Code Conversion, Journal on Computing (JoC), vol 1, No (4), 2012.
[9]
Maaz Bin Safeer Ahmad and Alvin Cheung, Optimizing Data-Intensive Applications Automatically By Leveraging Parallel Data Processing Frameworks, SIGMOD'17, May 14-19, 2017, Chicago, IL, USA, 1675--1678.
[10]
E.Gutiérrez, R.Asenjo, O.Plata and E.L.Zapata, Automatic parallelization of irregular applications, Parallel Computing, Volume 26, Issues 13--14, December 2000, Pages 1709--1738.
[11]
A. Barve, S. Khandelwal, N. Khan, S. Keshatiwar, S. Botre, Serial to Parallel Code Converter Tools A Review, International Journal of Research in Advent Technology (E-ISSN: 2321-9637) Special Issue National Conference "NCPCI-2016", 19 March 2016.

Cited By

View all
  • (2021)Refactoring Legacy Software for Layer SeparationInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402150006631:02(217-247)Online publication date: 2-Mar-2021
  • (2021)Dynamically Distributing Tasks from an Unattended Parallel Compiler with CloudbookHigh Performance Computing10.1007/978-3-030-68035-0_1(3-17)Online publication date: 3-Feb-2021

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ICISDM '18: Proceedings of the 2nd International Conference on Information System and Data Mining
April 2018
169 pages
ISBN:9781450363549
DOI:10.1145/3206098
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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 April 2018

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Legacy software application
  2. flexible architecture
  3. parallel code
  4. parallel programming model
  5. repository
  6. web service

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

ICISDM '18

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)6
  • Downloads (Last 6 weeks)0
Reflects downloads up to 22 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2021)Refactoring Legacy Software for Layer SeparationInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402150006631:02(217-247)Online publication date: 2-Mar-2021
  • (2021)Dynamically Distributing Tasks from an Unattended Parallel Compiler with CloudbookHigh Performance Computing10.1007/978-3-030-68035-0_1(3-17)Online publication date: 3-Feb-2021

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media