GPGPU performance evaluation of some basic molecular dynamics algorithms

Molecular dynamics is a computationally intensive problem but it is extremely amenable for parallel computation. Many-body potentials used for modeling of carbon and metallic nanostructures usually require much more computing resources than pair potentials. One of the ways to improve their performance is to transform them for running on computing systems that combines CPU and GPU. In this work OpenCL performance of basic molecular dynamics algorithms such as neighbor list generation along with different implementations of energy-force computation of Lennard-Jones, Tersoff and EAM potentials is evaluated. It is shown that concurrent memory writes are effective for Tersoff bond order potential and are not good for embedded-atom potential. Performance measurements show a significant GPU acceleration of basic molecular dynamics algorithms over the corresponding serial implementations.