Stochastic Speculative Computation Method and its Application to Monte Carlo Molecular Simulation

Monte Carlo (MC) molecular simulation has significant computational complexity, and parallel processing is considered effective for computation of problems with large complexity. In recent years, multicore or many-core processors have gained significant attention as they enable computation with a large degree of parallelism on desktop computers. However, in conventional parallel processing, processes must be synchronized frequently; thus, parallel computing is not necessarily efficient. In this study, we evaluate the effect of applying MultiStart-based speculative parallel computation to MC simulations. Using probability theory, we performed theoretical verification to determine if speculative computation is more effective than conventional parallel computation methods. The parameters obtained from the theoretical calculations were observed in experiments wherein the speculative method was applied to an MC molecular simulation. In this paper, we report the results of the theoretical verification and experiments, and we show that speculative computation can accelerate MC molecular simulations

Quantum computing of molecular magnet Mn12_{12}

Quantum computation in molecular magnets is studied by solving the time-dependent Schr\"{o}dinger equation numerically. Following Leuenberger and Loss (Nature (London) 410, 789(2001)), an external oscillating magnetic field is applied to populate and manipulate the spin coherent states in molecular magnet Mn$_{12}$. The conditions to realize parallel recording and reading data bases of Grover algorithsm in molecular magnets are discussed in details. It is found that an accurate duration time of magnetic pulse as well as the amplitudes are required to design the device of quantum computing.Comment: 3 pages, 1 figur

An Efficient Cell List Implementation for Monte Carlo Simulation on GPUs

Maximizing the performance potential of the modern day GPU architecture requires judicious utilization of available parallel resources. Although dramatic reductions can often be obtained through straightforward mappings, further performance improvements often require algorithmic redesigns to more closely exploit the target architecture. In this paper, we focus on efficient molecular simulations for the GPU and propose a novel cell list algorithm that better utilizes its parallel resources. Our goal is an efficient GPU implementation of large-scale Monte Carlo simulations for the grand canonical ensemble. This is a particularly challenging application because there is inherently less computation and parallelism than in similar applications with molecular dynamics. Consistent with the results of prior researchers, our simulation results show traditional cell list implementations for Monte Carlo simulations of molecular systems offer effectively no performance improvement for small systems [5, 14], even when porting to the GPU. However for larger systems, the cell list implementation offers significant gains in performance. Furthermore, our novel cell list approach results in better performance for all problem sizes when compared with other GPU implementations with or without cell lists.Comment: 30 page

Million-atom molecular dynamics simulation by order-N electronic structure theory and parallel computation

Parallelism of tight-binding molecular dynamics simulations is presented by means of the order-N electronic structure theory with the Wannier states, recently developed (J. Phys. Soc. Jpn. 69,3773 (2000)). An application is tested for silicon nanocrystals of more than millions atoms with the transferable tight-binding Hamiltonian. The efficiency of parallelism is perfect, 98.8 %, and the method is the most suitable to parallel computation. The elapse time for a system of $2\times 10^6$ atoms is 3.0 minutes by a computer system of 64 processors of SGI Origin 3800. The calculated results are in good agreement with the results of the exact diagonalization, with an error of 2 % for the lattice constant and errors less than 10 % for elastic constants.Comment: 5 pages, 3 figure

Accurate Reaction-Diffusion Operator Splitting on Tetrahedral Meshes for Parallel Stochastic Molecular Simulations

Spatial stochastic molecular simulations in biology are limited by the intense computation required to track molecules in space either in a discrete time or discrete space framework, meaning that the serial limit has already been reached in sub-cellular models. This calls for parallel simulations that can take advantage of the power of modern supercomputers; however exact methods are known to be inherently serial. We introduce an operator splitting implementation for irregular grids with a novel method to improve accuracy, and demonstrate potential for scalable parallel simulations in an initial MPI version. We foresee that this groundwork will enable larger scale, whole-cell stochastic simulations in the near future.Comment: 33 pages, 10 figure