INQ: a state-of-the art implementation of density functional theory for GPUs

ORAL

Abstract

In this talk I will present INQ, a new implementation of density functional theory (DFT) and time-dependent DFT (TDDFT) written from scratch to work on graphical processing units (GPUs).

Besides GPU support, INQ makes use of modern code design features and techniques, to make development fast and simple, and to ensure the quality of the program. By designing the code around algorithms, rather than against specific implementations and numerical libraries, we provide a concise and modular code that is simple to understand, flexible, and extensible.

What we achieve is a fairly complete DFT/TDDFT implementation in roughly 12,000 lines of open-source C++ code. It represents a modular platform for community-driven application development on emerging high-performance computing architectures. The code is freely accesible at http://gitlab.com/npneq/inq .

In TDDFT simulations on GPU-based supercomputers INQ achieves excellent performance. It can handle hundreds and thousands of atoms, with simulation times of a second or less per time-step, and scale to thousands of GPUs.

*The work was supported by the Center for Non-Perturbative Studies of Functional Materials Under Non-Equilibrium Conditions (NPNEQ) funded by the Computational Materials Sciences Program of the US Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division. Work by X.A, T.O and A.C was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. C.D.P was supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Materials Sciences and Engineering, under Contract No. DE-AC02-76SF00515 at SLAC.Computing support for this work came from the Lawrence Livermore National Laboratory Institutional Computing Grand Challenge program.

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Publication: X. Andrade, C. D. Pemmaraju, A. Kartsev, J. Xiao, A. Lindenberg, S. Rajpurohit, L. Z. Tan, T. Ogitsu, A. A. Correa, J. Chem. Theo. Comput., in press, 2021. Preprint: https://arxiv.org/abs/2106.03872

Presenters

  • Xavier Andrade

    • Lawrence Livermore Natl Lab

Authors

  • Xavier Andrade

    • Lawrence Livermore Natl Lab

  • Tadashi Ogitsu

    • Lawrence Livermore Natl Lab

  • Das Pemmaraju

    • SLAC National Accelerator Laboratory
    • SLAC Natl Accelerator Lab

  • Alfredo A Correa

    • Lawrence Livermore Natl Lab