The properties of polymer chain are simulated by self avoiding walk and bond fluctuation model. The parallel computing model is established to solve the physical problems of the polymer chains. We analyze the algorithms for polymer chains to parallel computing at high performance computing center. ...
Before considering the benefit of solving optimization problems in parallel, let’s briefly consider the simpler issue of running simulations in a parallel setting. To illustrate the effect of parallel computing on running multiple simulations, we will investigate a Mont...
“We used Parallel Computing Toolbox with MATLAB Parallel Server to distribute the work on a 56-processor cluster. This enabled us to rapidly identify an optimal neural network configuration using MATLAB and Deep Learning Toolbox, train the network using data from the transplantation databases, and...
Monte Carlo algorithms— A wide range of computational tasks that are processed by pseudorandom sampling of individual elements. DFT (Discrete Fourier Transform)— A widely-used technique for processing equally-spaced samples of a function or signal. Commonly used in DSP (Digital Signal Processing),...
Smith, J. R., “Parallel Algorithms for Depth First Searches: I. Planar Graphs,” International Conference on Parallel Processing, 1984. Google Scholar Solovay, R., and Strassen, V., “A Fast Monte-Carlo Test for Primality,” SIAM Journal of Computing, Vol. 6, 1977, pp. 84–85. Arti...
There are two varieties of the Jacobi-based algorithm (see section “SVD and Jacobi-based method”), one-sided and two-sided algorithms. The one-sided Jacobi algorithm is computationally more efficient than the two-sided algorithm23 and suitable for vector pipeline computing. Thus, to achieve ...
Many of the recent computational statistical analysis involve advanced algorithms with massive datasets and large numbers of parameters need to be estimated. In particular; DNA sequence analysis in bioinformatics (Vera, Jansen and Suppi 2008), bootstrap and Monte-Carlo simulations in multivariate time ...
Parallel Computing Toolbox™ is a tool that lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB...
PIRK is a tool to efficiently compute reachable sets for general nonlinear systems ofextremely high dimensions. It introduces three parallel algorithms for computing interval approximations of forward reachable sets, based on: component-wise contraction properties, mixed monotonicity, and Monte Carlo-based...
Features: Discusses development of algorithms for different applications plus other aspects related to parallel numerical solution of PDEs (e.g. grid refinement). Considers other numerical applications such as data retrieval by linear algebra approach and quasi Monte-Carlo methods. Covers molecular ...