INQ, a modern GPU-accelerated computational framework for (time-dependent) density functional theory

Year: 2021

Type: article

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.

Source: PubMed

DOI: 10.48550/arxiv.2106.03872

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

Institutions: Lawrence Livermore National Laboratory, SLAC National Accelerator Laboratory, Lawrence Berkeley National Laboratory, Molecular Foundry

Language: English

Topic: Advanced Chemical Physics Studies

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: U.S. Department of Energy, Office of Science, Basic Energy Sciences, Lawrence Livermore National Laboratory, SLAC National Accelerator Laboratory, Division of Materials Sciences and Engineering

OpenAlex ID: W3166827644