Litcius/Paper detail

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

Xavier Andrade, C. D. Pemmaraju, Alexey Kartsev, Jun Xiao, Aaron M. Lindenberg, Sangeeta Rajpurohit, Liang Z. Tan, Tadashi Ogitsu, Alfredo A. Correa

2021Journal of Chemical Theory and Computation29 citationsDOIOpen Access PDF

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.

Topics & Concepts

Modular designComputer scienceTime-dependent density functional theoryCode (set theory)ScratchImplementationParallel computingOn the flyDensity functional theoryComputational scienceComputer architectureProgramming languageOperating systemChemistrySet (abstract data type)Computational chemistryParallel Computing and Optimization TechniquesMachine Learning in Materials ScienceTheoretical and Computational Physics