GPU-accelerated simulation of colloidal suspensions with direct hydrodynamic interactions

TL;DR

This paper presents a GPU-accelerated implementation of Stokesian dynamics to simulate colloidal suspensions efficiently, avoiding explicit solvent modeling and focusing on memory access and numerical stability.

Contribution

It introduces a GPU-based algorithm for simulating hydrodynamic interactions in colloids, enhancing computational speed and stability compared to traditional methods.

Findings

Achieved efficient GPU implementation of hydrodynamic simulations

Demonstrated improved performance with memory access optimizations

Validated the approach with sedimentation of colloidal clusters

Abstract

Solvent-mediated hydrodynamic interactions between colloidal particles can significantly alter their dynamics. We discuss the implementation of Stokesian dynamics in leading approximation for streaming processors as provided by the compute unified device architecture (CUDA) of recent graphics processors (GPUs). Thereby, the simulation of explicit solvent particles is avoided and hydrodynamic interactions can easily be accounted for in already available, highly accelerated molecular dynamics simulations. Special emphasis is put on efficient memory access and numerical stability. The algorithm is applied to the periodic sedimentation of a cluster of four suspended particles. Finally, we investigate the runtime performance of generic memory access patterns of complexity $O(N^2)$ for various GPU algorithms relying on either hardware cache or shared memory.