Design and analysis of parallel Monte Carlo algorithms
This paper demonstrates that the potential of intrinsic parallelism in Monte Carlo methods, which has remained essentially untapped so far, can be exploited to implement these methods efficiently on SIMD and MIMD computers. Two basic static and dynamic computation assignment schemes are proposed for assigning the primary estimate computations (PECs) to processors in a parallel computer. These schemes can be used to design parallel Monte Carlo algorithms for many applications. The time complexity analyses of static computation assignment (SCA) schemes are carried out using some results from order statistics, whereas those of dynamic computation assignment (DCA) schemes are carried out using results from order statistics, renewal and queuing theories. It is shown that for smaller number of processors, linear speedup can be achieved with the SCA schemes and the speedup almost equal to the number of processors can be achieved with the DCA schemes. Some computational results for Monte Carlo solutions of Laplace*s equation are given to illustrate the performance of the various SCA and DCA schemes.