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Interlanguage parallel scripting for distributed-memory scientific computing

Authors:

Justin M. Wozniak,

Timothy G. Armstrong,

Ketan Maheshwari,

Daniel S. Katz,

Ian T. FosterAuthors Info & Claims

WORKS '15: Proceedings of the 10th Workshop on Workflows in Support of Large-Scale Science

Article No.: 6, Pages 1 - 11

https://doi.org/10.1145/2822332.2822338

Published: 15 November 2015 Publication History

Abstract

Scripting languages such as Python and R have been widely adopted as tools for the development of scientific software because of the expressiveness of the languages and their available libraries. However, deploying scripted applications on large-scale parallel computer systems such as the IBM Blue Gene/Q or Cray XE6 is a challenge because of issues including operating system limitations, interoperability challenges, and parallel filesystem overheads due to the small file system accesses common in scripted approaches. We present a new approach to these problems in which the Swift scripting system is used to integrate high-level scripts written in Python, R, and Tcl with native code developed in C, C++, and Fortran, by linking Swift to the library interfaces to the script interpreters. We present a technique to efficiently launch scripted applications on supercomputers, and we demonstrate high performance, such as invoking 14M Python interpreters per second on Blue Waters.

References

[1]

A. N. Adhikari, J. Peng, M. Wilde, J. Xu, K. F. Freed, and T. R. Sosnick. Modeling large regions in proteins: Applications to loops, termini, and folding. Protein Science, 21(1):107--121, 2012.

[2]

T. G. Armstrong, J. M. Wozniak, M. Wilde, and I. T. Foster. Compiler techniques for massively scalable implicit task parallelism. In Proc. SC, 2014.

Digital Library

[3]

D. M. Beazley. SWIG: An easy to use tool for integrating scripting languages with C and C++. In Proc. USENIX Tcl/Tk Workshop, 1996.

Digital Library

[4]

J. Bezanson, A. Edelman, S. Karpinski, and V. B. Shah. Julia: A fresh approach to numerical computing, 2014. http://arxiv.org/abs/1411.1607.

[5]

Celery: Distributed Task Queue. celeryproject.org.

[6]

B. Chamberlain, D. Callahan, and H. Zima. Parallel programmability and the Chapel language. Int. J. High Perform. Comput. Appl., 21(3):291--312, 2007.

Digital Library

[7]

T. W. Epperly, G. Kumfert, T. Dahlgren, D. Ebner, J. Leek, A. Prantl, and S. Kohn. High-performance language interoperability for scientific computing through Babel. J. of High-performance Computing Applications, 26(3), 2012.

Digital Library

[8]

M. Folk, R. McGrath, and N. Yeager. HDF: An update and future directions. In Proc. Geoscience and Remote Sensing Symposium, 1999.

[9]

R. Fourer, D. M. Gay, and B. Kernighan. AMPL: a mathematical programming language. In S. W. Wallace, editor, Algorithms and model formulations in mathematical programming, pages 150--151. Springer-Verlag New York, Inc., New York, NY, USA, 1989.

Digital Library

[10]

R. Gordon. Essential JNI: Java Native Interface. Prentice-Hall, Inc., 1998.

Digital Library

[11]

P. Klosowski, M. Koennecke, J. Tischler, and R. Osborn. NeXus: A common format for the exchange of neutron and synchrotron data. Physica B: Condensed Matter, 241--243:151--153, 1997.

[12]

S. J. Krieder and I. Raicu. Towards the support for many-task computing on many-core computing platforms. Doctoral Showcase, IEEE/ACM Supercomputing/SC, 2012.

[13]

M. Lunacek, J. Braden, and T. Hauser. The scaling of many-task computing approaches in Python on cluster supercomputers. In Proc. CLUSTER, 2013.

[14]

E. L. Lusk, S. C. Pieper, and R. M. Butler. More scalability, less pain: A simple programming model and its implementation for extreme computing. SciDAC Review, 17:30--37, January 2010.

[15]

J. McFarland. FortWrap web site. http://fortwrap.sourceforge.net.

[16]

R. Osborn. NeXpy web site. http://nexpy.github.io/nexpy.

[17]

F. Pérez and B. E. Granger. IPython: A system for interactive scientific computing. Comput. Sci. Eng., 9(3):21--29, May 2007.

Digital Library

[18]

J. C. Phillips, J. E. Stone, K. L. Vandivort, T. G. Armstrong, J. M. Wozniak, M. Wilde, and K. Schulten. Petascale Tcl with NAMD, VMD, and Swift/T. In Proc. High Performance Technical Computing in Dynamic Languages at SC, 2014.

Digital Library

[19]

A. Prantl, T. Epperly, S. Imam, and V. Sarkar. Interfacing Chapel with traditional HPC programming languages. In Proc. Partitioned Global Address Space Programming Models, 2011.

[20]

K. Price, R. Storn, and J. Lampinen. Differential evolution. Springer, 2005.

[21]

T. Proffen and R. Neder. DISCUS: A program for diffuse scattering and defect-structure simulation. Journal of Applied Crystallography, 30(2):171--175, 1997.

[22]

S. van der Walt, S. Colbert, and G. Varoquaux. The NumPy array: A structure for efficient numerical computation. Computing in Science Engineering, 13(2):22--30, 2011.

Digital Library

[23]

M. Wilde, M. Hategan, J. M. Wozniak, B. Clifford, D. S. Katz, and I. Foster. Swift: A language for distributed parallel scripting. Parallel Computing, 37:633--652, 2011.

Digital Library

[24]

J. M. Wozniak, T. G. Armstrong, K. Maheshwari, E. L. Lusk, D. S. Katz, M. Wilde, and I. T. Foster. Turbine: A distributed-memory dataflow engine for high performance many-task applications. Fundamenta Informaticae, 28(3), 2013.

Digital Library

[25]

J. M. Wozniak, T. G. Armstrong, K. C. Maheshwari, D. S. Katz, M. Wilde, and I. T. Foster. Toward interlanguage parallel scripting for distributed-memory scientific computing. In Proc. CLUSTER, 2015.

Digital Library

[26]

J. M. Wozniak, T. G. Armstrong, M. Wilde, D. S. Katz, E. Lusk, and I. T. Foster. Swift/T: Scalable data flow programming for many-task applications. In Proc. CCGrid, 2013.

Digital Library

[27]

J. M. Wozniak, T. Peterka, T. G. Armstrong, J. Dinan, E. Lusk, M. Wilde, and I. Foster. Dataflow coordination of data-parallel tasks via MPI 3.0. In Proc. EuroMPI, 2013.

Digital Library

Cited By

Hategan-Marandiuc MMerzky ACollier NMaheshwari KOzik JTurilli MWilke AWozniak JChard KFoster Ida Silva RJha SLaney D(2023)PSI/J: A Portable Interface for Submitting, Monitoring, and Managing Jobs2023 IEEE 19th International Conference on e-Science (e-Science)10.1109/e-Science58273.2023.10254912(1-10)Online publication date: 9-Oct-2023
https://doi.org/10.1109/e-Science58273.2023.10254912
Dorier MWozniak JRoss RMontagnat JTaylor IGesing SSakellariou R(2017)Supporting task-level fault-tolerance in HPC workflows by launching MPI jobs inside MPI jobsProceedings of the 12th Workshop on Workflows in Support of Large-Scale Science10.1145/3150994.3151001(1-11)Online publication date: 12-Nov-2017
https://dl.acm.org/doi/10.1145/3150994.3151001
Ozik JCollier NWozniak JSpagnuolo CHuschka TChick S(2016)From desktop to large-scale model exploration with Swift/TProceedings of the 2016 Winter Simulation Conference10.5555/3042094.3042132(206-220)Online publication date: 11-Dec-2016
https://dl.acm.org/doi/10.5555/3042094.3042132
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cover image ACM Conferences

WORKS '15: Proceedings of the 10th Workshop on Workflows in Support of Large-Scale Science

November 2015

98 pages

ISBN:9781450339896

DOI:10.1145/2822332

Conference Chairs:
Johan Montagnat
CNRS, France
,
Ian Taylor
Cardiff University, UK

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 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].

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SIGHPC: ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing
SIGARCH: ACM Special Interest Group on Computer Architecture
IEEE-CS\DATC: IEEE Computer Society

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 November 2015

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U.S. Department of Energy

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SC15

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SIGHPC
SIGARCH
IEEE-CS\DATC

SC15: The International Conference for High Performance Computing, Networking, Storage and Analysis

November 15, 2015

Texas, Austin

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WORKS '15 Paper Acceptance Rate 9 of 13 submissions, 69%;

Overall Acceptance Rate 30 of 54 submissions, 56%

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Cited By

Hategan-Marandiuc MMerzky ACollier NMaheshwari KOzik JTurilli MWilke AWozniak JChard KFoster Ida Silva RJha SLaney D(2023)PSI/J: A Portable Interface for Submitting, Monitoring, and Managing Jobs2023 IEEE 19th International Conference on e-Science (e-Science)10.1109/e-Science58273.2023.10254912(1-10)Online publication date: 9-Oct-2023
https://doi.org/10.1109/e-Science58273.2023.10254912
Dorier MWozniak JRoss RMontagnat JTaylor IGesing SSakellariou R(2017)Supporting task-level fault-tolerance in HPC workflows by launching MPI jobs inside MPI jobsProceedings of the 12th Workshop on Workflows in Support of Large-Scale Science10.1145/3150994.3151001(1-11)Online publication date: 12-Nov-2017
https://dl.acm.org/doi/10.1145/3150994.3151001
Ozik JCollier NWozniak JSpagnuolo CHuschka TChick S(2016)From desktop to large-scale model exploration with Swift/TProceedings of the 2016 Winter Simulation Conference10.5555/3042094.3042132(206-220)Online publication date: 11-Dec-2016
https://dl.acm.org/doi/10.5555/3042094.3042132
Wozniak JWilde MFoster I(2016)Challenges and Opportunities for Dataflow Processing on Exascale ComputersProceedings of the Sixth Workshop on Data-Flow Execution Models for Extreme Scale Computing10.1145/3292533.3292537(1-5)Online publication date: 15-Sep-2016
https://dl.acm.org/doi/10.1145/3292533.3292537
Ozik JCollier NWozniak JSpagnuolo C(2016)From desktop to Large-Scale Model Exploration with Swift/T2016 Winter Simulation Conference (WSC)10.1109/WSC.2016.7822090(206-220)Online publication date: Dec-2016
https://doi.org/10.1109/WSC.2016.7822090

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