A High-Performance Parallel-Generalized Born Implementation Enabled by Tabulated Interaction Rescaling
2010 (English)In: Journal of Computational Chemistry, ISSN 0192-8651, E-ISSN 1096-987X, Vol. 31, no 14, 2593-2600 p.Article in journal (Refereed) Published
Implicit solvent representations, in general, and generalized Born models, in particular, provide an attractive way to reduce the number of interactions and degrees of freedom in a system. The instantaneous relaxation of the dielectric shielding provided by an implicit solvent model can be extremely efficient for high-throughput and Monte Carlo studies, and a reduced system size can also remove a lot of statistical noise. Despite these advantages, it has been difficult for generalized Born implementations to significantly outperform optimized explicit-water simulations due to more complex functional forms and the two extra interaction stages necessary to calculate Born radii and the derivative chain rule terms contributing to the force. Here, we present a method that uses a rescaling transformation to make the standard generalized Born expression a function of a single variable, which enables an efficient tabulated implementation on any modern CPU hardware. The total performance is within a factor 2 of simulations in vacuo. The algorithm has been implemented in Gromacs, including single-instruction multiple-data acceleration, for three different Born radius models and corresponding chain rule terms. We have also adapted the model to work with the virtual interaction sites commonly used for hydrogens to enable long-time steps, which makes it possible to achieve a simulation performance of 0.86 μs/day for BBA5 with 1-nm cutoff on a single quad-core desktop processor. Finally, we have also implemented a set of streaming kernels without neighborlists to accelerate the non-cutoff setup occasionally used for implicit solvent simulations of small systems.
Place, publisher, year, edition, pages
2010. Vol. 31, no 14, 2593-2600 p.
generalized born, tabulation, molecular dynamics
IdentifiersURN: urn:nbn:se:su:diva-38246DOI: 10.1002/jcc.21552ISI: 000282309800007OAI: oai:DiVA.org:su-38246DiVA: diva2:308383