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Title: 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];  [2];  [3]; ORCiD logo [1]
  1. Division of Mathematics and Computer Science, Argonne National Laboratory, Lemont, IL, USA
  2. Department of Computer Science, California State University, Fullerton, Fullerton, CA, USA
  3. Department of Computer and Information Science, University of Oregon, Eugene, OR, USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1582735
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 ID: ISSN 1094-3420
Publisher:
SAGE Publications
Country of Publication:
United States
Language:
English

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. doi: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. doi: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 = ,
volume = ,
place = {United States},
year = {2020},
month = {1}
}

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