Accelerating molecular modeling applications with graphics processors

By John E. Stone, James C. Phillips, P. Freddolino, David J. Hardy, Leonardo G. Trabuco, Klaus Schulten. Journal of Computational Chemistry, 2007

736 citations recorded by the source corpus.

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Program optimization space pruning for a multithreaded gpu (2008, methodology): “This kernel is derived from the “Unroll8y” kernel in [23].”
GPU Computing (2008, methodology): “State-of-the-art GPUs offer new opportunities for accelerating computationally demanding analyses on desktop computers, which previously required batch mode runs on clusters or supercomputers [30].”
On the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods (2009, background): “Examples include Stone et al. (2007) and Friedrichs et al. (2009), which discuss their use in molecular modelling and dynamics.”
Accounting for receptor flexibility and enhanced sampling methods in computer aided drug design (2013, methodology): “Popular simulation packages like NAMD (37), AMBER c , GROMACS , and ACEMD b have released code, which runs on GPUs.”
Optimization principles and application performance evaluation of a multithreaded GPU using CUDA (2008, methodology): “Based on direct Coulomb summation, as described in [24].”
Scalable molecular dynamics on CPU and GPU architectures with NAMD. (2020, methodology): “III), 21 and support for AMD and Intel GPUs is being developed as part of readiness programs for the Oak Ridge Fron-tier and Argonne Aurora exascale machines.”