A GPU-accelerated Direct-sum Boundary Integral Poisson-Boltzmann Solver

In this paper, we present a GPU-accelerated direct-sum boundary integral method to solve the linear Poisson-Boltzmann (PB) equation. In our method, a well-posed boundary integral formulation is used to ensure the fast convergence of Krylov subspace based linear algebraic solver such as the GMRES. The molecular surfaces are discretized with flat triangles and centroid collocation. To speed up our method, we take advantage of the parallel nature of the boundary integral formulation and parallelize the schemes within CUDA shared memory architecture on GPU. The schemes use only $11N+6N_c$ size-of-double device memory for a biomolecule with $N$ triangular surface elements and $N_c$ partial charges. Numerical tests of these schemes show well-maintained accuracy and fast convergence. The GPU implementation using one GPU card (Nvidia Tesla M2070) achieves 120-150X speed-up to the implementation using one CPU (Intel L5640 2.27GHz). With our approach, solving PB equations on well-discretized molecular surfaces with up to 300,000 boundary elements will take less than about 10 minutes, hence our approach is particularly suitable for fast electrostatics computations on small to medium biomolecules

Acceleration of Coarse Grain Molecular Dynamics on GPU Architectures

Coarse grain (CG) molecular models have been proposed to simulate complex sys- tems with lower computational overheads and longer timescales with respect to atom- istic level models. However, their acceleration on parallel architectures such as Graphic Processing Units (GPU) presents original challenges that must be carefully evaluated. The objective of this work is to characterize the impact of CG model features on parallel simulation performance. To achieve this, we implemented a GPU-accelerated version of a CG molecular dynamics simulator, to which we applied specic optimizations for CG models, such as dedicated data structures to handle dierent bead type interac- tions, obtaining a maximum speed-up of 14 on the NVIDIA GTX480 GPU with Fermi architecture. We provide a complete characterization and evaluation of algorithmic and simulated system features of CG models impacting the achievable speed-up and accuracy of results, using three dierent GPU architectures as case studie

Air pollution modelling using a graphics processing unit with CUDA

The Graphics Processing Unit (GPU) is a powerful tool for parallel computing. In the past years the performance and capabilities of GPUs have increased, and the Compute Unified Device Architecture (CUDA) - a parallel computing architecture - has been developed by NVIDIA to utilize this performance in general purpose computations. Here we show for the first time a possible application of GPU for environmental studies serving as a basement for decision making strategies. A stochastic Lagrangian particle model has been developed on CUDA to estimate the transport and the transformation of the radionuclides from a single point source during an accidental release. Our results show that parallel implementation achieves typical acceleration values in the order of 80-120 times compared to CPU using a single-threaded implementation on a 2.33 GHz desktop computer. Only very small differences have been found between the results obtained from GPU and CPU simulations, which are comparable with the effect of stochastic transport phenomena in atmosphere. The relatively high speedup with no additional costs to maintain this parallel architecture could result in a wide usage of GPU for diversified environmental applications in the near future.Comment: 5 figure

GPU-accelerated simulation of colloidal suspensions with direct hydrodynamic interactions

Solvent-mediated hydrodynamic interactions between colloidal particles can significantly alter their dynamics. We discuss the implementation of Stokesian dynamics in leading approximation for streaming processors as provided by the compute unified device architecture (CUDA) of recent graphics processors (GPUs). Thereby, the simulation of explicit solvent particles is avoided and hydrodynamic interactions can easily be accounted for in already available, highly accelerated molecular dynamics simulations. Special emphasis is put on efficient memory access and numerical stability. The algorithm is applied to the periodic sedimentation of a cluster of four suspended particles. Finally, we investigate the runtime performance of generic memory access patterns of complexity $O(N^2)$ for various GPU algorithms relying on either hardware cache or shared memory.Comment: to appear in a special issue of Eur. Phys. J. Special Topics on "Computer Simulations on GPUs