Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 1. Generalized Born - PubMed (original) (raw)

. 2012 May 8;8(5):1542-1555.

doi: 10.1021/ct200909j. Epub 2012 Mar 26.

Free PMC article

Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 1. Generalized Born

Andreas W Götz et al. J Chem Theory Comput. 2012.

Free PMC article

Abstract

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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Figures

Figure 1

Figure 1

Peak floating-point operations per second (Flop/s; left) and memory bandwidth (right) for Intel CPUs and NVIDIA GPUs.

Figure 2

Figure 2

Schematic representation of the work-load distribution for the calculation of nonbond forces with N atoms. Each square represents the interactions between two atoms i and j for which the resulting forces need to be evaluated. These are grouped together in tiles of size W × W that are each assigned to an independent warp. Due to symmetry, only the blue diagonal tiles and the green off-diagonal tiles need to be considered for the calculation. For details, see the text.

Figure 3

Figure 3

Total energy (kcal/mol) along constant energy trajectories using a time step of 0.5 fs without constraints. Shown are results for TRPCage (left) and ubiquitin (right) for different precision models. The insets show the first nanosecond of each trajectory.

Figure 4

Figure 4

Root-mean-square deviations (RMSDs) of the Cα backbone carbon atoms of ubiquitin (excluding the flexible tail, residues 71–76) with respect to the crystal structure for 50 independent trajectories as obtained with the CPU implementation and the GPU implementation of PMEMD using different precision models.

Figure 5

Figure 5

Root-mean-square fluctuations (RMSFs) of the Cα backbone carbon atoms of ubiquitin residues 71–76 with respect to the crystal structure for 50 independent trajectories of 100 ns length as obtained with the CPU implementation and the GPU implementation of PMEMD using different precision models.

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