Cited By
View all- Akli LAlameda JGordon SMadrid MRivera LGaither K(2016)XSEDE TrainingProceedings of the XSEDE16 Conference on Diversity, Big Data, and Science at Scale10.1145/2949550.2949586(1-6)Online publication date: 17-Jul-2016
Leveraging DiaGrid hub for interactively generating and running parallel programs
Article No.: 44, Pages 1 - 8
Abstract
Interactive Parallelization Tool (IPT) is a semi-automatic tool that can be used by domain experts and students for transforming certain classes of existing applications into multiple parallel variants. An end-user of IPT provides existing application and high-level specifications for parallelization as input. On the basis of the specifications provided by the end-user, IPT carries out the code changes in the given existing application to generate parallel variants that can be run on different High Performance Computing (HPC) platforms. The parallel programming paradigms that are currently supported by IPT are MPI, OpenMP, and CUDA. The supported base languages are C and C++. Though IPT is still under active development, it has been recently made available on a web-enabled platform, named DiaGrid Hub, with the support from the XSEDE Extended Collaborative Support Service (ECSS). While the main goal of IPT is to make parallel programming easy for its end-users, the main goal of DiaGrid Hub is to enable the research community with instant access to HPC and High Throughput Computing platforms through a user-friendly web-interface. By deploying IPT on DiaGrid Hub, our goal is to enable the end-users to generate parallel versions of their existing applications without having to install IPT locally. They can also immediately compile and run the generated applications on Purdue and XSEDE resources that are available through DiaGrid Hub. Hence, the collaborative project that is reported in this paper lowers the entry-barriers to parallel programming and the usage of the national CyberInfrastructure (CI). In this paper, we present our ongoing work on deploying IPT over DiaGrid and testing the usability of IPT through a web-interface.
References
[1]
William Gropp, Ewing Lusk, Anthony Skjellum, "Using MPI: Portable Parallel Programming with the Message Passing Interface", MIT Press, 1999, pp. 1--371.
[2]
The OpenMP API specification for parallel programming: accessed on January 27, 2015: http://openmp.org/wp/
[3]
What is CUDA, accessed on January 27, 2015: https://developer.nvidia.com/what-cuda
[4]
Rajeev Thakur, Pavan Balaji, Darius Buntinas, David Goodell, William Gropp, Torsten Hoefler, Sameer Kumar, Ewing Lusk, Jesper Larsson Träff, "MPI at Exascale", presented at SciDAC 2010: http://htor.inf.ethz.ch/publications/img/mpi_exascale.pdf
[5]
DiaGrid Hub homepage, accessed on April 3, 2015: https://diagrid.org
[6]
Michael McLennan, Rick Kennell, "HUBzero: A Platform for Dissemination and Collaboration in Computational Science and Engineering," Computing in Science and Engineering, Vol. 12, No. 2, 2010, pp. 48--52.
[7]
Michael McLennan, Steven Clark, Ewa Deelman, Mats Rynge, Karan Vahi, Frank McKenna, Derrick Kearney, Carol Song, "HUBzero and Pegasus: integrating scientific workflows into science gateways", Concurrency and Computation Practice and Experience, Vol. 27, No. 2, 2014, pp. 328--343.
[8]
XSEDE, Extreme Science and engineering Discovery Environment website, accessed on March 16, 2015: https://www.xsede.org/
[9]
Ritu Arora, Julio Olaya, and Madhav Gupta, "A Tool for Interactive Parallelization", In Proceedings of the 2014 Annual Conference on Extreme Science and Engineering Discovery Environment (XSEDE '14). ACM, New York, NY, USA, Article 51, 8 pages. DOI=10.1145/2616498.2616558 http://doi.acm.org/10.1145/2616498.2616558
[10]
ROSE homepage, accessed on January 29, 2015: http://www.rosecompiler.org/
[11]
Timothy G. Mattson, Beverly A. Sanders, and Berna L. Massingill, Patterns for Parallel Programming, Addison Wesley Professional, ISBN-10: 0321228111, 2004, pp. 1--384.
[12]
Ritu Arora, Ejenio Capetillo, Purushotham Bangalore, and Marjan Mernik, "A high-level framework for parallelizing legacy applications for multiple platforms", In Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery (XSEDE '13). ACM, New York, NY, USA, Article 58, 8 pages. DOI=10.1145/2484762.2484829 http://doi.acm.org/10.1145/2484762.2484829
[13]
IPT tool page on DiaGrid, accessed on January 29, 2015: https://diagrid.org/tools/ipt
[14]
Molecular Dynamics program webpage, accessed on April 3, 2015: http://people.sc.fsu.edu/~jburkardt/cpp_src/md/md.html
[15]
Madhav Gupta, "Interactive Parallelization Tool - Parallelizing Molecular Dynamics with MPI" video, accessed on April 5, 2015: https://www.youtube.com/watch?v=HvlA4pnfFjE
[16]
C. S. Ierotheou, Steve P. Johnson, M. Cross and P. F. Leggett, "Computer aided parallelisation tools (CAPTools) - conceptual overview and performance on the parallelisation of structured mesh codes", Parallel Computing, Volume 22, No. 2, 1996, pp. 163--195.
[17]
Parallel Software Products, ParaWise website, accessed on April 3, 2015: http://www.parallelsp.com/
[18]
Markus Püschel, José M. F. Moura, Jeremy Johnson, David Padua, Manuela Veloso, Bryan Singer, Jianxin Xiong, Franz Franchetti, Aca Gacic, Yevgen Voronenko, Kang Chen, Robert W. Johnson, Nicholas Rizzolo, "SPIRAL: Code Generation for DSP Transforms," Proceedings of the IEEE, Special issue on Program Generation, Optimization, and Adaptation, Vol. 93, No. 2, 2005, pp. 232--275.
[19]
Boyana Norris, Albert Hartono, and William Gropp, "Annotations for productivity and performance portability", In Petascale Computing: Algorithms and Applications, Computational Science, 2007, pp. 443--462.
[20]
High Performance Computing (HPC) Systems at the Texas Advanced Computing Center, accessed on January 29, 2015: https://www.tacc.utexas.edu/systems/overview
Index Terms
- Leveraging DiaGrid hub for interactively generating and running parallel programs
Recommendations
Enabling dynamic and intelligent workflows for HPC, data analytics, and AI convergence
AbstractThe evolution of High-Performance Computing (HPC) platforms enables the design and execution of progressively larger and more complex workflow applications in these systems. The complexity comes not only from the number of elements that compose ...
Highlights- Analysis of the HPC, Big Data and AI convergence in complex scientific workflows.
- Description of use cases where complex workflows combining HPC, DA and AI are required.
- Present challenges of delivering a new workflow platform to ...
Compiling data-parallel programs for clusters of SMPs: Research Articles
Compilers for Parallel ComputersClusters of shared-memory multiprocessors (SMPs) have become the most promising parallel computing platforms for scientific computing. However, SMP clusters significantly increase the complexity of user application development when using the low-level ...
Parallelizing Subroutines in Sequential Programs
An algorithm for making sequential programs parallel is described, which first identifies all subroutines, then determines the appropriate execution mode and restructures the code. It works recursively to parallelize the entire program. We use Fortran ...
Comments
Information & Contributors
Information
Published In
Copyright © 2015 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 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]
Sponsors
- San Diego Super Computing Ctr: San Diego Super Computing Ctr
- HPCWire: HPCWire
- Omnibond: Omnibond Systems, LLC
- SGI
- Internet2
- Indiana University: Indiana University
- CASC: The Coalition for Academic Scientific Computation
- NICS: National Institute for Computational Sciences
- Intel: Intel
- DDN: DataDirect Networks, Inc
- DELL
- CORSA: CORSA Technology
- ALLINEA: Allinea Software
- Cray
- RENCI: Renaissance Computing Institute
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 26 July 2015
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
XSEDE '15
Sponsor:
- San Diego Super Computing Ctr
- HPCWire
- Omnibond
- Indiana University
- CASC
- NICS
- Intel
- DDN
- CORSA
- ALLINEA
- RENCI
XSEDE '15: Extreme Science Engineering Discovery Environment 2015 Conference
July 26 - 30, 2015
Missouri, St. Louis
Acceptance Rates
XSEDE '15 Paper Acceptance Rate 49 of 70 submissions, 70%;
Overall Acceptance Rate 129 of 190 submissions, 68%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 65Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 16 Jan 2025
Other Metrics
Citations
Cited By
View all- Akli LAlameda JGordon SMadrid MRivera LGaither K(2016)XSEDE TrainingProceedings of the XSEDE16 Conference on Diversity, Big Data, and Science at Scale10.1145/2949550.2949586(1-6)Online publication date: 17-Jul-2016
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in