# LAMMPS-KOKKOS: Performance Portable Molecular Dynamics Across Exascale Architectures

arXiv: https://arxiv.org/abs/2508.13523 · HTML: https://mlsanity.com/paper/2508.13523/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2025.

## Built on by (most cited first, 1 of 1)

- [BranchIP: Learning Adaptive Equivariant Computation for Interatomic Potentials](https://mlsanity.com/paper/2610.02013/) (2026)

## Abstract (arXiv)

Since its inception in 1995, LAMMPS has grown to be a world-class molecular dynamics code, with thousands of users, over one million lines of code, and multi-scale simulation capabilities. We discuss how LAMMPS has adapted to the modern heterogeneous computing landscape by integrating the Kokkos performance portability library into the existing C++ code. We investigate performance portability of simple pairwise, many-body reactive, and machine-learned force-field interatomic potentials. We present results on GPUs across different vendors and generations, and analyze performance trends, probing FLOPS throughput, memory bandwidths, cache capabilities, and thread-atomic operation performance. Finally, we demonstrate strong scaling on three exascale machines -- OLCF Frontier, ALCF Aurora, and NNSA El Capitan -- as well as on the CSCS Alps supercomputer, for the three potentials.