A high-performance GPU implementation of the electron-phonon Wannier interpolation and the related transport properties

TL;DR

This paper introduces a GPU-accelerated implementation of the electron-phonon Wannier interpolation method, significantly speeding up calculations of transport properties like conductivity while maintaining accuracy, thus enabling efficient simulations for materials.

Contribution

The paper presents a novel GPU-based implementation of the EPWI method, achieving over 170x speedup compared to CPU algorithms and establishing a versatile framework for EPI-related property computations.

Findings

GPU implementation achieves 173x speedup over CPU

Conductivity of aluminum computed in 20 minutes on a single GPU

Method maintains consistency with CPU-based results

Abstract

The electron-phonon Wannier interpolation (EPWI) method is an efficient way to compute the properties of electron-phonon interactions (EPIs) accurately. This study presents a GPU-accelerated implementation of the EPWI method for computing transport properties, followed by a performance analysis. The implementation is tested on common systems such as aluminum and silicon. The results show complete consistency with those obtained through CPU computations. The proposed algorithm has the capability of computing the conductivity of aluminum in 20 minutes on a single NVIDIA Tesla V100 GPU, adopting a $200^3$ electron and phonon sampling grid. This speed is 173 times higher than the CPU-based algorithm, running on two nodes of the Intel Xeon Platinum 8260 CPU. Such impressive acceleration is achieved by carefully designing the algorithm to exploit the GPU's specific features. Furthermore, this methodology establishes a generic foundation for EPWI algorithms, which can be applied to other EPI-related properties.