GPGPU Acceleration of All-Electron Electronic Structure Theory Using Localized Numeric Atom-Centered Basis Functions

TL;DR

This paper introduces a GPGPU-accelerated all-electron density-functional theory implementation using localized basis functions, achieving significant speedups in large-scale electronic structure calculations on parallel architectures.

Contribution

It presents a novel GPGPU-based implementation of all-electron DFT with domain decomposition for parallel real-space operations, enabling efficient large-scale simulations.

Findings

Speedups of 4.5 to 6.6 in SCF iterations with GPGPU acceleration

Overall speedup of 3-4 for entire calculations on multi-atom systems

Scalability demonstrated for large 375-atom Bi2Se3 bilayer

Abstract

We present an implementation of all-electron density-functional theory for massively parallel GPGPU-based platforms, using localized atom-centered basis functions and real-space integration grids. Special attention is paid to domain decomposition of the problem on non-uniform grids, which enables compute- and memory-parallel execution across thousands of nodes for real-space operations, e.g. the update of the electron density, the integration of the real-space Hamiltonian matrix, and calculation of Pulay forces. To assess the performance of our GPGPU implementation, we performed benchmarks on three different architectures using a 103-material test set. We find that operations which rely on dense serial linear algebra show dramatic speedups from GPGPU acceleration: in particular, SCF iterations including force and stress calculations exhibit speedups ranging from 4.5 to 6.6. For the architectures and problem types investigated here, this translates to an expected overall speedup between 3-4 for the entire calculation (including non-GPU accelerated parts), for problems featuring several tens to hundreds of atoms. Additional calculations for a 375-atom Bi$_2$Se$_3$ bilayer show that the present GPGPU strategy scales for large-scale distributed-parallel simulations.