Computer simulation of liquids
Computer simulation of liquids
Fast parallel algorithms for short-range molecular dynamics
Journal of Computational Physics
Scalable algorithms for molecular dynamics simulations on commodity clusters
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Streaming Algorithms for Biological Sequence Alignment on GPUs
IEEE Transactions on Parallel and Distributed Systems
General purpose molecular dynamics simulations fully implemented on graphics processing units
Journal of Computational Physics
GPU accelerated Monte Carlo simulation of the 2D and 3D Ising model
Journal of Computational Physics
Quantifying NUMA and contention effects in multi-GPU systems
Proceedings of the Fourth Workshop on General Purpose Processing on Graphics Processing Units
Parallel verlet neighbor list algorithm for GPU-optimized MD simulations
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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Molecular dynamics simulation is a powerful tool to simulate and analyze complex physical processes and phenomena at atomic characteristic for predicting the natural time-evolution of a system of atoms. Precise simulation of physical processes has strong requirements both in the simulation size and computing timescale. Therefore, finding available computing resources is crucial to accelerate computation. However, a tremendous computational resource (GPGPU) are recently being utilized for general purpose computing due to its high performance of floating-point arithmetic operation, wide memory bandwidth and enhanced programmability. As for the most time-consuming component in MD simulation calculation during the case of studying liquid metal solidification processes, this paper presents a fine-grained spatial decomposition method to accelerate the computation of update of neighbor lists and interaction force calculation by take advantage of modern graphics processors units (GPU), enlarging the scale of the simulation system to a simulation system involving 10@?000@?000 atoms. In addition, a number of evaluations and tests, ranging from executions on different precision enabled-CUDA versions, over various types of GPU (NVIDIA 480GTX, 580GTX and M2050) to CPU clusters with different number of CPU cores are discussed. The experimental results demonstrate that GPU-based calculations are typically 9~11 times faster than the corresponding sequential execution and approximately 1.5~2 times faster than 16 CPU cores clusters implementations. On the basis of the simulated results, the comparisons between the theoretical results and the experimental ones are executed, and the good agreement between the two and more complete and larger cluster structures in the actual macroscopic materials are observed. Moreover, different nucleation and evolution mechanism of nano-clusters and nano-crystals formed in the processes of metal solidification is observed with large-sized system.