A parallel algorithm for random walk construction with application to the Monte Carlo solution of partial differential equations

Random walks are widely applicable in statistical and scientific computations. In particular, they are used in the Monte Carlo method to solve elliptic and parabolic partial differential equations (PDEs). This method holds several advantages over other methods for PDEs as it solves problems with irregular boundaries and/or discontinuities, gives solutions at individual points, and exhibits great parallelism. However, the generation of each random walk in the Monte Carlo method has been done sequentially because each point in the walk is derived from the preceding point by moving one grid step along a randomly selected direction. A parallel algorithm for random walk generation in regular as well as irregular regions is presented. The algorithm is based on parallel prefix computations. The communication structure of the algorithm is shown to ideally fit on a hypercube of n nodes, where n is the number of processors