Sparse LU factorization of the Cray T3D

The paper describes a parallel algorithm for the LU factorization of sparse matrices on distributed memory machines by using SPMD as programming model and PVM as message passing interface. We address all the difficulties arising in sparse codes, as the fill-in or the dynamic movement of data inside the matrix. The cyclic distribution has been used to evenly distribute the elements onto a mesh of processors, whereas two local storage schemes are proposed: A semi-ordered and two-dimensional linked list, which fulfils better the requirements of the algorithm, and a compressed storage by rows, which behaves better in the use of memory. The: properties of the code are extensively analyzed and execution times on the GRAY T3D are presented to illustrate the overall efficiency achieved by our methods.