Abstract

The performance of a fully parallel direct solver for large sparse-symmetric positive definite systems of linear equations is demonstrated. The solver is designed for distributed-memory, message-passing parallel computer systems. All phases of the computation, including sym bolic processing as well as numeric factorization and triangular solution, are performed in parallel. A parallel Cartesian-nested dissection algorithm is used to compute a fill- reducing ordering for the matrix and an appropriate partitioning of the problem across the processors. The separator tree resulting from nested dissection is used to identify and exploit large-grain parallelism in the remaining steps of the computation. The parallel performance of the solver is reported for a series of test problems on the Thinking Machines CM-5 and the Intel Touchstone Delta. The parallel efficiency, scalability, and absolute perfor mance of the solver, as well as the relative importance of the various phases of the computation, are investigated empirically

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.