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Title: GPU acceleration of the Locally Selfconsistent Multiple Scattering code for first principles calculation of the ground state and statistical physics of materials

Abstract

The Locally Self-consistent Multiple Scattering (LSMS) code solves the first principles Density Functional theory Kohn–Sham equation for a wide range of materials with a special focus on metals, alloys and metallic nano-structures. It has traditionally exhibited near perfect scalability on massively parallel high performance computer architectures. In this paper, we present our efforts to exploit GPUs to accelerate the LSMS code to enable first principles calculations of O(100,000) atoms and statistical physics sampling of finite temperature properties. We reimplement the scattering matrix calculation for GPUs with a block matrix inversion algorithm that only uses accelerator memory. Finally, using the Cray XK7 system Titan at the Oak Ridge Leadership Computing Facility we achieve a sustained performance of 14.5PFlop/s and a speedup of 8.6 compared to the CPU only code.

Authors:
 [1];  [2];  [2];  [2];  [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. NVIDIA Corporation, Santa Clara, CA (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
Contributing Org.:
NVIDIA Corporation, Santa Clara, CA (United States)
OSTI Identifier:
1335344
Alternate Identifier(s):
OSTI ID: 1396465
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Computer Physics Communications
Additional Journal Information:
Journal Volume: 211; Journal ID: ISSN 0010-4655
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; First-principles; Monte-Carlo; Phase transitions

Citation Formats

Eisenbach, Markus, Larkin, Jeff, Lutjens, Justin, Rennich, Steven, and Rogers, James H. GPU acceleration of the Locally Selfconsistent Multiple Scattering code for first principles calculation of the ground state and statistical physics of materials. United States: N. p., 2016. Web. https://doi.org/10.1016/j.cpc.2016.07.013.
Eisenbach, Markus, Larkin, Jeff, Lutjens, Justin, Rennich, Steven, & Rogers, James H. GPU acceleration of the Locally Selfconsistent Multiple Scattering code for first principles calculation of the ground state and statistical physics of materials. United States. https://doi.org/10.1016/j.cpc.2016.07.013
Eisenbach, Markus, Larkin, Jeff, Lutjens, Justin, Rennich, Steven, and Rogers, James H. Tue . "GPU acceleration of the Locally Selfconsistent Multiple Scattering code for first principles calculation of the ground state and statistical physics of materials". United States. https://doi.org/10.1016/j.cpc.2016.07.013. https://www.osti.gov/servlets/purl/1335344.
@article{osti_1335344,
title = {GPU acceleration of the Locally Selfconsistent Multiple Scattering code for first principles calculation of the ground state and statistical physics of materials},
author = {Eisenbach, Markus and Larkin, Jeff and Lutjens, Justin and Rennich, Steven and Rogers, James H.},
abstractNote = {The Locally Self-consistent Multiple Scattering (LSMS) code solves the first principles Density Functional theory Kohn–Sham equation for a wide range of materials with a special focus on metals, alloys and metallic nano-structures. It has traditionally exhibited near perfect scalability on massively parallel high performance computer architectures. In this paper, we present our efforts to exploit GPUs to accelerate the LSMS code to enable first principles calculations of O(100,000) atoms and statistical physics sampling of finite temperature properties. We reimplement the scattering matrix calculation for GPUs with a block matrix inversion algorithm that only uses accelerator memory. Finally, using the Cray XK7 system Titan at the Oak Ridge Leadership Computing Facility we achieve a sustained performance of 14.5PFlop/s and a speedup of 8.6 compared to the CPU only code.},
doi = {10.1016/j.cpc.2016.07.013},
journal = {Computer Physics Communications},
number = ,
volume = 211,
place = {United States},
year = {2016},
month = {7}
}

Journal Article:

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Cited by: 3 works
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