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Efficient parallel generation of many-nucleon basis for large-scale ab initio nuclear structure calculations
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SYSNO ASEP 0491174 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Efficient parallel generation of many-nucleon basis for large-scale ab initio nuclear structure calculations Author(s) Langr, D. (CZ)
Dytrych, Tomáš (UJF-V) ORCID, SAI
Oberhuber, T. (CZ)
Knapp, F. (CZ)Number of authors 4 Source Title Lecture Notes in Computer Science, Parallel Processing and Applied Mathematics. - Berlin : Springer Verlag, 2018 - ISSN 0302-9743 - ISBN 978-3-319-78054-2 Pages s. 341-350 Number of pages 10 s. Publication form Print - P Action 12th Internatnional Conference on Parallel Processing and Aplied Mathematics (PPAM 2017) Event date 10.09.2017 - 13.09.2017 VEvent location Czestochowa Country PL - Poland Event type WRD Language eng - English Country DE - Germany Keywords Ab initio ; basis generation ; many-nucleon basis ; nuclear structure ; parallel algorithm Subject RIV BA - General Mathematics OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects GA16-16772S GA ČR - Czech Science Foundation (CSF) Institutional support UJF-V - RVO:61389005 UT WOS 000458563900032 EID SCOPUS 85044764561 DOI 10.1007/978-3-319-78054-2_32 Annotation We address the problem of generating a many-nucleon basis for ab initio nuclear structure modeling, which quickly becomes a significant runtime bottleneck for large model spaces. We first analyze the original basis generation algorithm, which does not employ multi-threading parallel paradigm. Based on the analysis, we propose and empirically evaluate a new efficient scalable basis generation algorithm. We report a reduction of basis generation runtime by a factor of 42 on the Blue Waters supercomputer and by two orders of magnitude on our test-bed computer system with Broadwell CPUs. Workplace Nuclear Physics Institute Contact Markéta Sommerová, sommerova@ujf.cas.cz, Tel.: 266 173 228 Year of Publishing 2019
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