Parallel quasi-Monte Carlo methods on a heterogeneous cluster

We introduce a new parallelization scheme for low-discrepancy sequences. Our approach is similar to the parameterization approach used for pseudorandom number generators. We present a theoretical analysis of this scheme and compare it numerically with the conventional blocking and leap-frogging parallelization strategies, when they are applied to problems from option pricing and transport theory. The numerical results suggest that our scheme might be very useful especially in distributed computing environments with unreliable and heterogeneous clusters.