Large-Scale Benchmark of Electronic Structure Solvers with the ELSI Infrastructure

ORAL

Abstract

Routine application of electronic structure theory to systems consisting of thousands of atoms is often hindered by the solution of an eigenproblem. We here present an update to the ELectronic Structure Infrastructure (ELSI), an open-source software interface to facilitate the implementation and optimal use of high-performance solver libraries covering cubic scaling eigensolvers, linear scaling density-matrix-based algorithms, and other reduced scaling methods in between. The ELSI interface has been integrated into four electronic structure code projects (DFTB+, DGDFT, FHI-aims, SIESTA), forming the foundation of our effort to rigorously benchmark the performance of the solvers on equal footing. This presentation will particularly focus on a systematic set of large-scale benchmarks for multiple solvers performed with Kohn-Sham density-functional theory and density-functional tight-binding theory. Factors that strongly affect the efficiency of the solvers are identified and analyzed, including system size and dimensionality, matrix sparsity, eigenspectrum width, number of MPI processes, etc. Based on these benchmarks, we discuss our strategy to automatically select a solver for an arbitrary problem.

Presenters

  • Victor Yu

    Department of Mechanical Engineering and Materials Science, Duke University

Authors

  • Victor Yu

    Department of Mechanical Engineering and Materials Science, Duke University

  • William Dawson

    Center for Computational Science, RIKEN, Japan

  • Alberto Garcia

    Laboratorio de Estructura Electronica de Materiales, Institut de Ciencia de Materials de Barcelona, Spain

  • Ville Havu

    Department of Applied Physics, Aalto University, Finland

  • Ben Hourahine

    Department of Physics, University of Strathclyde, Scotland

  • William P Huhn

    Duke University, Department of Mechanical Engineering and Materials Science, Duke University

  • Mathias Jacquelin

    Computational Research Division, Lawrence Berkeley National Laboratory

  • Weile Jia

    Department of Mathematics, University of California, Berkeley

  • Murat Keceli

    Argonne Leadership Computing Facility, Argonne National Laboratory

  • Raul Laasner

    Department of Mechanical Engineering and Materials Science, Duke University

  • Yingzhou Li

    Department of Mathematics, Duke University

  • Lin Lin

    Department of Mathematics, University of California, Berkeley

  • Jianfeng Lu

    Duke University, Department of Mathematics, Duke University

  • Jose Roman

    Departament de Sistemes Informatics i Computacio, Universitat Politecnica de Valencia, Spain

  • Alvaro Vazquez-Mayagoitia

    Argonne National Lab, Argonne Leadership Computing Facility, Argonne National Laboratory, Argonne National Labs

  • Chao Yang

    Computational Research Division, Lawrence Berkeley National Laboratory, Lawrence Berkeley National Laboratory

  • Volker Blum

    Duke University, Mechanical Engineering & Materials Science, Duke University, Mechanical Engineering and Materials Science, Duke University, Department of Mechanical Engineering and Materials Science, Duke University