StochasticGW-GPU: rapid quasi-particle energies for molecules beyond 10000 atoms

TL;DR

StochasticGW-GPU is a highly efficient, GPU-accelerated code that computes quasi-particle energies for extremely large molecules with tens of thousands of atoms, achieving high precision in minutes.

Contribution

The paper introduces StochasticGW-GPU, a GPU-accelerated implementation that significantly improves performance for large-scale GW calculations beyond previous versions.

Findings

Able to compute band gaps for systems with over 10,000 atoms

Achieves statistical precision better than ±0.03 eV

Performs calculations within minutes for large molecules

Abstract

$\mathtt{StochasticGW}$ is a code for computing accurate Quasi-Particle (QP) energies of molecules and material systems in the GW approximation. $\mathtt{StochasticGW}$ utilizes the stochastic Resolution of the Identity (sROI) technique to enable a massively-parallel implementation with computational costs that scale semi-linearly with system size, allowing the method to access systems with tens of thousands of electrons. We introduce a new implementation, $\mathtt{StochasticGW-GPU}$, for which the main bottleneck steps have been ported to GPUs and which gives substantial performance improvements over previous versions of the code. We showcase the new code by computing band gaps of hydrogenated silicon clusters ($\textrm{S}\textrm{i}_{\textrm{x}}\textrm{H}_{\textrm{y}}$) containing up to 10001 atoms and 35144 electrons, and we obtain individual QP energies with a statistical precision of better than $\pm0.03$ eV with times-to-solution on the order of minutes.