GPU-Accelerated Molecular Modeling Coming Of Age

By John E. Stone, David J. Hardy, Ivan S. Ufimtsev, Klaus Schulten. Journal of Molecular Graphics and Modelling, 2010

419 citations recorded by the source corpus.

Graphics processing units (GPUs) have traditionally been used in molecular modeling solely for visualization of molecular structures and animation of trajectories resulting from molecular dynamics simulations. Modern GPUs have evolved into fully programmable, massively parallel co-processors that can now be exploited to accelerate many scientific computations, typically providing about one order of magnitude speedup over CPU code and in special cases providing speedups of two orders of magnitude. This paper surveys the development of molecular modeling algorithms that leverage GPU computing, the advances already made and remaining issues to be resolved, and the continuing evolution of GPU technology that promises to become even more useful to molecular modeling. Hardware acceleration with commodity GPUs is expected to benefit the overall computational biology community by bringing teraflops performance to desktop workstations and in some cases potentially changing what were formerly batch-mode computational jobs into interactive tasks.

Subfields: Computer Networks and Communications, Hardware and Architecture, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/544c4502ef6533dffb831d69aa07c50ef0cfbca3

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Citations in the corpus

SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties (2017, background): “…understanding of the underlying chemical concepts. first factor is being addressed with development of GPU-accelerated code for molecular modeling [36] and special-purpose chemistry supercomputers [34], but the second factor requires solutions grounded in further fundamental research, which is…”
Scalable molecular dynamics on CPU and GPU architectures with NAMD. (2020, methodology): “42 NAMD was the first fully featured MD package to exploit GPU acceleration, 21 and it was also a pioneer in supporting GPU-accelerated clusters.”
Systematic Validation of Protein Force Fields against Experimental Data (2012, methodology): “Thanks to recent developments in computing hardware [2–4] and in methods to distribute [5] and parallelize [6] simulations or enhance sampling efficiency [7], it is now possible to simulate directly protein dynamics on the millisecond timescale [8] or to reconstruct longtimescale behavior from short”
Weighted Ensemble Simulation: Review of Methodology, Applications, and Software (2017, positive): “Dramatic improvements in achievable computing speed based on hardware advances (44, 56, 60) have transformed the field of biomolecular simulation.”
Molecular Docking: Shifting Paradigms in Drug Discovery (2019, background): “Finally, GPU computing made big data-driven computation tasks accessible to a larger public [201], and it is expected to play a prominent role, not only in docking but in future in silico drug design in general [202].”