Efficient implementation of the quasiparticle self-consistent $GW$ method on GPU

TL;DR

This paper presents a GPU-accelerated, maintainable implementation of the quasiparticle self-consistent GW method for electronic structure calculations, enabling efficient and scalable first-principles simulations.

Contribution

The authors developed a multi-GPU version of QSGW within the ecalj package using modern Fortran, improving performance and code sustainability.

Findings

Achieved significant speedup in QSGW calculations on GPU clusters.

Demonstrated scalability and efficiency through benchmark tests.

Enabled more accessible high-accuracy electronic excitation computations.

Abstract

We have developed a multi-GPU version of the quasiparticle self-consistent $GW$ (QSGW), a cutting-edge method for describing electronic excitations in a first-principles approach. While the QSGW calculation algorithm is inherently well-suited for GPU computation due to its reliance on large-scale tensor operations, achieving a maintainable and extensible implementation is not straightforward. Addressing this, we have developed a GPU version within the \texttt{ecalj} package, utilizing module-based programming style in modern Fortran. This design facilitates future development and code sustainability. Following the summary of the QSGW formalism, we present our GPU implementation approach and the results of benchmark calculations for two types of systems to demonstrate the capability of our GPU-supported QSGW calculations.