Parallel solutions for sparse matrix computations

This thesis analyzes typical computational challenges raised by sparse matrix implementations and proposes solutions for solving them, and proposes a method that enables the selection of the best performance-yielding allocations over the entire range of operational parameters, prior to actually executing the computations.