We present an implementation of explicit solvent all atom classical molecular dynamics (MD) within the AMBER program package that runs entirely on CUDA-enabled GPUs. First released publicly in April 2010 as part of version 11 of the AMBER MD package and further improved and optimized over the last two years, this implementation supports the three most widely used statistical mechanical ensembles (NVE, NVT, and NPT), uses particle mesh Ewald (PME) for the long-range electrostatics, and runs entirely on CUDA-enabled NVIDIA graphics processing units (GPUs), providing results that are statistically indistinguishable from the traditional CPU version of the software and with performance that exceeds that achievable by the CPU version of AMBER software running on all conventional CPU-based clusters and supercomputers. We briefly discuss three different precision models developed specifically for this work (SPDP, SPFP, and DPDP) and highlight the technical details of the approach as it extends beyond previously reported work [Götz et al., J. Chem. Theory Comput. 2012, DOI: 10.1021/ct200909j; Le Grand et al., Comp. Phys. Comm. 2013, DOI: 10.1016/j.cpc.2012.09.022].We highlight the substantial improvements in performance that are seen over traditional CPU-only machines and provide validation of our implementation and precision models. We also provide evidence supporting our decision to deprecate the previously described fully single precision (SPSP) model from the latest release of the AMBER software package.
We present an implementation of generalized Born implicit
solvent
all-atom classical molecular dynamics (MD) within the AMBER program
package that runs entirely on CUDA enabled NVIDIA graphics processing
units (GPUs). We discuss the algorithms that are used to exploit the
processing power of the GPUs and show the performance that can be
achieved in comparison to simulations on conventional CPU clusters.
The implementation supports three different precision models in which
the contributions to the forces are calculated in single precision
floating point arithmetic but accumulated in double precision (SPDP),
or everything is computed in single precision (SPSP) or double precision
(DPDP). In addition to performance, we have focused on understanding
the implications of the different precision models on the outcome
of implicit solvent MD simulations. We show results for a range of
tests including the accuracy of single point force evaluations and
energy conservation as well as structural properties pertainining
to protein dynamics. The numerical noise due to rounding errors within
the SPSP precision model is sufficiently large to lead to an accumulation
of errors which can result in unphysical trajectories for long time
scale simulations. We recommend the use of the mixed-precision SPDP
model since the numerical results obtained are comparable with those
of the full double precision DPDP model and the reference double precision
CPU implementation but at significantly reduced computational cost.
Our implementation provides performance for GB simulations on a single
desktop that is on par with, and in some cases exceeds, that of traditional
supercomputers.
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