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Abstract
The timescales of biological processes, primarily those inherent to the molecular mechanisms of disease, are long (>μs) and involve complex interactions of systems consisting of many atoms (>106). Simulating these systems requires an advanced computational approach, and as such, coarse-grained (CG) models have been developed and highly optimised for accelerator hardware, primarily graphics processing units (GPUs). In this review, I discuss the implementation of CG models for biologically relevant systems, and show how such models can be optimised and perform well on GPU-accelerated hardware. Several examples of GPU implementations of CG models for both molecular dynamics and Monte Carlo simulations on purely GPU and hybrid CPU/GPU architectures are presented. Both the hardware and algorithmic limitations of various models, which depend greatly on the application of interest, are discussed.
Acknowledgements
This project was supported by start-up funds from Yeshiva University, and by a hardware grant through the professor partnership from the Nvidia Corporation. Computational resources were provided by the National Science Foundation through XSEDE resources from grant number TG-MCB120160. The author is grateful to Michael L. Klein (Temple University) for his support and Athanassios Z. Panagiotopoulos (Princeton University) for his collaboration. The author would also like to thank Benjamin G. Levine (Michigan State University), Wataru Shinoda (AIST), Russell H. DeVane (Procter & Gamble Research), Axel Kohlmeyer (Temple University), Arben Jusufi (College of Staten Island), Samantha Sanders (Exxon Mobil Corp.), Steve Barr (Air Force Research Lab) and Joshua Anderson (University of Michigan) for their collaboration and many useful discussions over the course of these projects.