Parallel solution of sparse linear systems on a vector multiprocessor computer

An efficient approach is described for solving sparse linear systems using direct methods on a shared-memory vector multiprocessor computer. Parallelism is accomplished by using a nested bordered block diagonal matrix partitioning technique. A nested block structure is used to represent the sparse matrix, making possible the use of vectorization to achieve high performance. This approach is suitable for many applications that require the repeated direct solution of sparse linear systems with identical matrix structure, such as circuit simulation. The approach has been implemented in a program that runs on an ALLIANT FX/8 vector multiprocessor with shared memory. The performance of the program is described