A parallel MCSA implementation and our numerical test framework will
allow us to compare its performance to Krylov methods and allow us to
experiment with the way we combine it with the Neumann-Ulam solver. We
hypothesized that if we can identify the amount of overlap in our Neumann-
Ulam experiments required to eliminate most transitions between adjacent
domains owned by different processors, then perhaps transport to adjacent
domains can be ignored. This will result in a biased estimator for the MCSA
correction that cannot be reproduced, however, these studies will aim to
determine if that bias and lack of reproducibility affects the numerical results
and reproducibility of the MCSA method, yielding a critical outcome for
this work. Furthermore, we will use the experimental framework to measure
the convergence rates for non-symmetric systems and memory usage of the
MCSA algorithm and compare them to conventional Krylov methods using
the same framework.