Student Cluster Competition 2017, Team University ofTexas at Austin/Texas State University: Reproducing Vectorization of the Tersoff Multi-Body Potential on the Intel Skylake and NVIDIA V100 Architectures

TL;DR

This paper reproduces and evaluates the vectorization performance of the Tersoff multi-body potential kernel in LAMMPS on Intel Skylake and NVIDIA V100 architectures, focusing on accuracy, optimization, and scalability.

Contribution

It demonstrates the reproducibility of vectorization results for the Tersoff potential on modern CPU and GPU architectures, providing insights into performance and scaling.

Findings

Achieved reproducible vectorization results for Tersoff potential

Analyzed accuracy and optimization performance on Skylake and V100

Assessed scalability of the implementation

Abstract

This paper satisfies the reproducibility challenge of the Student Cluster Competition at Supercomputing 2017. We attempted to reproduce the results of H\"{o}hnerbach et al. (2016) for an implementation of a vectorized code for the Tersoff multi-body potential kernel of the molecular dynamics code Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). We investigated accuracy, optimization performance, and scaling with our Intel CPU and NVIDIA GPU based cluster.