Unified Parallel Algorithms for Gaussian Elimination with Backward Substitution on Product Networks

The increasing interest in product networks (PNs) as a method of combining desirable properties of component networks, has prompted a need for the general study of the algorithmic issues related to this important class of interconnection networks. In this paper we present unified parallel algorithms for Gaussian elimination, with partial and complete pivoting, on product networks. A parallel algorithm for backward substitution is also presented. The proposed algorithms are network independent and are also independent of the matrix distribution methods employed. These algorithms can be used on a wide range of PNs including hypercube, mesh, and k-ary n-cube. Unified models for estimating computation time and interprocessor communication time are also presented. These models are then used to measure the performance of the proposed algorithms on several product networks.