# Quantized bounding volume hierarchies for neighbor search in molecular simulations on graphics processing units

## TL;DR

This paper introduces a quantized BVH-based neighbor search algorithm for molecular simulations on GPUs, significantly improving speed over traditional grid methods, and recommends its use for efficient neighbor list generation.

## Contribution

The paper presents a novel quantized BVH approach that accelerates neighbor search in molecular simulations on GPUs, outperforming existing grid-based methods.

## Key findings

- Neighbor search with quantized BVH is 2-4 times faster than state-of-the-art grid methods.
- Quantized BVH provides a more efficient neighbor list generation in molecular simulations.
- The proposed method is recommended over traditional cell list approaches for GPU-based simulations.

## Abstract

We present an algorithm for neighbor search in molecular simulations on graphics processing units (GPUs) based on bounding volume hierarchies (BVHs). The BVH is compressed into a low-precision, quantized representation to increase the BVH traversal speed compared to a previous implementation. We find that neighbor search using the quantized BVH is roughly two to four times faster than current state-of-the-art methods using uniform grids (cell lists) for a suite of benchmarks for common molecular simulation models. Based on the benchmark results, we recommend using the BVH instead of a single cell list for neighbor list generation in molecular simulations on GPUs.