On-Board Multi-GPU Molecular Dynamics

Abstract

Molecular dynamics simulations allow us to study the behavior of complex biomolecular systems. These simulations suffer a large computational complexity that leads to simulation times of several weeks in order to recreate just a few microseconds of a molecule's motion even on high-performance computing platforms. In recent years, state-of-the-art molecular dynamics algorithms have benefited from the parallel computing capabilities of multicore systems, as well as GPUs used as co-processors. In this paper we present a parallel molecular dynamics algorithm for on-board multi-GPU architectures. We parallelize a state-of-the-art molecular dynamics algorithm at two levels. We employ a spatial partitioning approach to simulate the dynamics of one portion of a molecular system on each GPU, and we take advantage of direct communication between GPUs to transfer data among portions. We also parallelize the simulation algorithm to exploit the multi-processor computing model of GPUs. Most importantly, we present novel parallel algorithms to update the spatial partitioning and set up transfer data packages on each GPU. We demonstrate the feasibility and scalability of our proposal through a comparative study with NAMD, a well known parallel molecular dynamics implementation.