Inq, a Modern GPU-Accelerated Computational Framework for (Time-Dependent) Density Functional Theory

Xavier Andrade; Chaitanya Das Pemmaraju; Alexey Kartsev; Jun Xiao; Aaron Lindenberg; Sangeeta Rajpurohit; Liang Z Tan; Tadashi Ogitsu; Alfredo A Correa

doi:10.1021/acs.jctc.1c00562

Inq, a Modern GPU-Accelerated Computational Framework for (Time-Dependent) Density Functional Theory

J Chem Theory Comput. 2021 Dec 14;17(12):7447-7467. doi: 10.1021/acs.jctc.1c00562. Epub 2021 Nov 2.

Authors

Xavier Andrade¹, Chaitanya Das Pemmaraju², Alexey Kartsev², Jun Xiao², Aaron Lindenberg², Sangeeta Rajpurohit³, Liang Z Tan³, Tadashi Ogitsu¹, Alfredo A Correa¹

Affiliations

¹ Quantum Simulations Group, Lawrence Livermore National Laboratory, Livermore, California 94551, United States.
² Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States.
³ The Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.

PMID: 34726888
DOI: 10.1021/acs.jctc.1c00562

Abstract

We present inq, a new implementation of density functional theory (DFT) and time-dependent DFT (TDDFT) written from scratch to work on graphic processing units (GPUs). Besides GPU support, inq makes use of modern code design features and takes advantage of newly available hardware. By designing the code around algorithms, rather than against specific implementations and numerical libraries, we aim to provide a concise and modular code. The result is a fairly complete DFT/TDDFT implementation in roughly 12 000 lines of open-source C++ code representing a modular platform for community-driven application development on emerging high-performance computing architectures.