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Title: Python in the NERSC Exascale Science Applications Program for Data

Abstract

We describe a new effort at the National Energy Re- search Scientific Computing Center (NERSC) in performance analysis and optimization of scientific Python applications targeting the Intel Xeon Phi (Knights Landing, KNL) many- core architecture. The Python-centered work outlined here is part of a larger effort called the NERSC Exascale Science Applications Program (NESAP) for Data. NESAP for Data focuses on applications that process and analyze high-volume, high-velocity data sets from experimental/observational science (EOS) facilities supported by the US Department of Energy Office of Science. We present three case study applications from NESAP for Data that use Python. These codes vary in terms of “Python purity” from applications developed in pure Python to ones that use Python mainly as a convenience layer for scientists without expertise in lower level programming lan- guages like C, C++ or Fortran. The science case, requirements, constraints, algorithms, and initial performance optimizations for each code are discussed. Our goal with this paper is to contribute to the larger conversation around the role of Python in high-performance computing today and tomorrow, highlighting areas for future work and emerging best practices

Authors:
; ; ; ; ; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1412701
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Conference
Resource Relation:
Conference: 2017 International Conference for High Performance Computing, Networking, Storage and Analysis, 11/12/17 - 11/17/17, Denver , CO, US
Country of Publication:
United States
Language:
English

Citation Formats

Ronaghi, Zahra, Thomas, Rollin, Deslippe, Jack, Bailey, Stephen, Gursoy, Doga, Kisner, Theodore, Keskitalo, Reijo, and Borrill, Julian. Python in the NERSC Exascale Science Applications Program for Data. United States: N. p., 2017. Web. doi:10.1145/3149869.3149873.
Ronaghi, Zahra, Thomas, Rollin, Deslippe, Jack, Bailey, Stephen, Gursoy, Doga, Kisner, Theodore, Keskitalo, Reijo, & Borrill, Julian. Python in the NERSC Exascale Science Applications Program for Data. United States. https://doi.org/10.1145/3149869.3149873
Ronaghi, Zahra, Thomas, Rollin, Deslippe, Jack, Bailey, Stephen, Gursoy, Doga, Kisner, Theodore, Keskitalo, Reijo, and Borrill, Julian. 2017. "Python in the NERSC Exascale Science Applications Program for Data". United States. https://doi.org/10.1145/3149869.3149873. https://www.osti.gov/servlets/purl/1412701.
@article{osti_1412701,
title = {Python in the NERSC Exascale Science Applications Program for Data},
author = {Ronaghi, Zahra and Thomas, Rollin and Deslippe, Jack and Bailey, Stephen and Gursoy, Doga and Kisner, Theodore and Keskitalo, Reijo and Borrill, Julian},
abstractNote = {We describe a new effort at the National Energy Re- search Scientific Computing Center (NERSC) in performance analysis and optimization of scientific Python applications targeting the Intel Xeon Phi (Knights Landing, KNL) many- core architecture. The Python-centered work outlined here is part of a larger effort called the NERSC Exascale Science Applications Program (NESAP) for Data. NESAP for Data focuses on applications that process and analyze high-volume, high-velocity data sets from experimental/observational science (EOS) facilities supported by the US Department of Energy Office of Science. We present three case study applications from NESAP for Data that use Python. These codes vary in terms of “Python purity” from applications developed in pure Python to ones that use Python mainly as a convenience layer for scientists without expertise in lower level programming lan- guages like C, C++ or Fortran. The science case, requirements, constraints, algorithms, and initial performance optimizations for each code are discussed. Our goal with this paper is to contribute to the larger conversation around the role of Python in high-performance computing today and tomorrow, highlighting areas for future work and emerging best practices},
doi = {10.1145/3149869.3149873},
url = {https://www.osti.gov/biblio/1412701}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2017},
month = {11}
}

Conference:
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