Understanding the landscape of scientific software used on high-performance computing platforms
Abstract
Scientific discovery increasingly relies on computation through simulations, analytics, and machine and deep learning. Of these, simulations on high-performance computing (HPC) platforms have been the cornerstone of scientific computing for more than two decades. However, the development of simulation software has, in general, occurred through accretion, with a few exceptions. With an increase in scientific understanding, models have become more complex, rendering an accretion mode untenable to the point where software productivity and sustainability have become active concerns in scientific computing. In this survey paper, we examine a modest set of HPC scientific simulation applications that are already using cutting-edge HPC platforms. Several have been in existence for a decade or more. Our objective in this survey is twofold: first, to understand the landscape of scientific computing on HPC platforms in order to distill the currently scattered knowledge about software practices that have helped both developer and software productivity, and second, to understand the kind of tools and methodologies that need attention for continued productivity.
- Authors:
-
[1]
- Division of Mathematics and Computer Science, Argonne National Laboratory, Lemont, IL, USA
- Department of Computer Science, California State University, Fullerton, Fullerton, CA, USA
- Department of Computer and Information Science, University of Oregon, Eugene, OR, USA
- Publication Date:
- Research Org.:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1582735
- Alternate Identifier(s):
- OSTI ID: 1774616
- Grant/Contract Number:
- AC02-06CH11357; 17-SC-20-SC
- Resource Type:
- Published Article
- Journal Name:
- International Journal of High Performance Computing Applications
- Additional Journal Information:
- Journal Name: International Journal of High Performance Computing Applications Journal Volume: 34 Journal Issue: 4; Journal ID: ISSN 1094-3420
- Publisher:
- SAGE Publications
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; community code; computational software; software engineering; software productivity
Citation Formats
Grannan, A., Sood, K., Norris, B., and Dubey, A. Understanding the landscape of scientific software used on high-performance computing platforms. United States: N. p., 2020.
Web. doi:10.1177/1094342019899451.
Grannan, A., Sood, K., Norris, B., & Dubey, A. Understanding the landscape of scientific software used on high-performance computing platforms. United States. https://doi.org/10.1177/1094342019899451
Grannan, A., Sood, K., Norris, B., and Dubey, A. Tue .
"Understanding the landscape of scientific software used on high-performance computing platforms". United States. https://doi.org/10.1177/1094342019899451.
@article{osti_1582735,
title = {Understanding the landscape of scientific software used on high-performance computing platforms},
author = {Grannan, A. and Sood, K. and Norris, B. and Dubey, A.},
abstractNote = {Scientific discovery increasingly relies on computation through simulations, analytics, and machine and deep learning. Of these, simulations on high-performance computing (HPC) platforms have been the cornerstone of scientific computing for more than two decades. However, the development of simulation software has, in general, occurred through accretion, with a few exceptions. With an increase in scientific understanding, models have become more complex, rendering an accretion mode untenable to the point where software productivity and sustainability have become active concerns in scientific computing. In this survey paper, we examine a modest set of HPC scientific simulation applications that are already using cutting-edge HPC platforms. Several have been in existence for a decade or more. Our objective in this survey is twofold: first, to understand the landscape of scientific computing on HPC platforms in order to distill the currently scattered knowledge about software practices that have helped both developer and software productivity, and second, to understand the kind of tools and methodologies that need attention for continued productivity.},
doi = {10.1177/1094342019899451},
journal = {International Journal of High Performance Computing Applications},
number = 4,
volume = 34,
place = {United States},
year = {Tue Jan 14 00:00:00 EST 2020},
month = {Tue Jan 14 00:00:00 EST 2020}
}
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https://doi.org/10.1177/1094342019899451
https://doi.org/10.1177/1094342019899451
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