Domain decomposition models for parallel Monte Carlo transport

We present a strategy for parallelizing computations that use the transport method. It combines spatial domain decomposition with domain replication t o realize the scaling benefits of replication while allowing for problems w hose computational mesh will not fit in a single processor's memory. The me sh is decomposed into a small number of spatial domains-typically fewer dom ains than there are processors-and heuristics are used to estimate the comp utational effort required to generate the solution in each subdomain using Monte Carlo. That work estimate determines the number of times a subdomain is replicated relative to the others. Timing of runs for two problems show that the new method scales better than traditional domain decomposition.