Parallel processing in computational stochastic dynamics

Abstract Studying large complex problems that often arise in computational stochastic dynamics (CSD) demands significant computer power and data storage. Parallel processing can help meet these requirements by exploiting the computational and storage capabilities of multiprocessing computational environments. The challenge is to develop parallel algorithms and computational strategies that can take full advantage of parallel machines. This paper reviews some of the characteristics of parallel computing and the techniques used to parallelize computational algorithms in CSD. The characteristics of parallel processor environments are discussed, including parallelization through the use of message passing and parallelizing compilers. Several applications of parallel processing in CSD are then developed: solutions of the Fokker–Planck equation, Monte Carlo simulation of dynamical systems, and random eigenvector problems. In these examples, parallel processing is seen to be a promising approach through which to resolve some of the computational issues pertinent to CSD.

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