Hybrid programming-model strategies for GPU offloading of electronic structure calculation kernels

  • Jean Luc Fattebert
  • , Christian F.A. Negre
  • , Joshua Finkelstein
  • , Jamaludin Mohd-Yusof
  • , Daniel Osei-Kuffuor
  • , Michael E. Wall
  • , Yu Zhang
  • , Nicolas Bock
  • , Susan M. Mniszewski

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

To address the challenge of performance portability and facilitate the implementation of electronic structure solvers, we developed the basic matrix library (BML) and Parallel, Rapid O(N), and Graph-based Recursive Electronic Structure Solver (PROGRESS) library. The BML implements linear algebra operations necessary for electronic structure kernels using a unified user interface for various matrix formats (dense and sparse) and architectures (CPUs and GPUs). Focusing on density functional theory and tight-binding models, PROGRESS implements several solvers for computing the single-particle density matrix and relies on BML. In this paper, we describe the general strategies used for these implementations on various computer architectures, using OpenMP target functionalities on GPUs, in conjunction with third-party libraries to handle performance critical numerical kernels. We demonstrate the portability of this approach and its performance in benchmark problems.

Original languageEnglish
Article number122501
JournalJournal of Chemical Physics
Volume160
Issue number12
DOIs
StatePublished - Mar 28 2024

Funding

This manuscript has been authored in part by UT-Battelle, LLC, under Contract No. DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This work was also performed by members of the Los Alamos National Laboratory. The Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of the U.S. Department of Energy (Contract No. 89233218NCA000001). Portions of this work were performed under the auspices of the U.S. Department of Energy by the Lawrence Livermore National Laboratory under Contract No. DE-AC52-07NA27344. This work was performed as part of the Co-design Center for Particle Applications, supported by the Exascale Computing Project (No. 17-SC-20-SC), a collaborative effort of the DOE Office of Science and the National Nuclear Security Administration (NNSA). This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

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