High Performance GPU Accelerated MuST Software
Xiao Liang, Edward Hanna, Derek Simmel, Hang Liu, Yang Wang

TL;DR
This paper presents GPU acceleration techniques for the MuST software, significantly speeding up electronic structure calculations for large solid systems by offloading matrix inverse computations to GPUs.
Contribution
The paper introduces a GPU-based acceleration method for the LSMS algorithm in MuST, focusing on matrix inverse computations to enhance performance.
Findings
Achieved significant speedup in NiAu alloy calculations
Demonstrated effective GPU offloading for matrix inverse tasks
Improved computational efficiency for large-scale electronic structure calculations
Abstract
The MuST package is a computational software designed for ab initio electronic structure calculations for solids. The Locally Self-consistent Multiple Scattering (LSMS) method implemented in MuST allows to perform the electronic structure calculation for systems with a large number of atoms per unit cell. For the LSMS method with muffin-tin potential approximation, the major computational challenge is the matrix inverse for the scattering matrix calculation, which could take more than 90\% of the computing time. However, the matrix inverse can be significantly accelerated by modern graphical-processing-units (GPUs). In this paper, we discuss our approach to the code acceleration by offloading the matrix inverse tasks to the GPUs through a Fortran-C interface from the Fortran code to the CUDA code. We report our performance results showing significant speedup ratio achieved to the…
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Taxonomy
TopicsMachine Learning in Materials Science · Advanced Chemical Physics Studies · Chemical and Physical Properties of Materials
