Abstract
The Continuum-configurational Bias Monte Carlo algorithm (CBMC) is a very efficient method for the simulation of polymer melts. This algorithm is well-suited to parallelization on shared-memory multiprocessors. The main effort for parallelization is often spent in the decomposition of the data structures and the design of the parallel program sections. We present some generally applicable methods to improve the optimization process on shared-memory multiprocessors using global address space and on distributed-memory multicomputers using message passing.
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