A domain-decomposing parallel sparse linear system solver

A domain-decomposing parallel sparse linear system solver

The solution of large sparse linear systems is often the most time-consuming part of many science and engineering applications. Computational fluid dynamics, circuit simulation, power network analysis, and material science are just a few examples of the application areas in which large sparse linear systems need to be solved effectively. In this paper we introduce a new parallel hybrid sparse l...

PSPIKE: A Parallel Hybrid Sparse Linear System Solver

The availability of large-scale computing platforms comprised of tens of thousands of multicore processors motivates the need for the next generation of highly scalable sparse linear system solvers. These solvers must optimize parallel performance, processor (serial) performance, as well as memory requirements, while being robust across broad classes of applications and systems. In this paper, ...

PSPIKE Parallel General Sparse Linear System Solver Manual Version 1.0.1

A Large-Grain Parallel Sparse System Solver

The eeciency of solving sparse linear systems on parallel processors and more complex multicluster architectures such as Cedar is greatly enhanced if relatively large grain computational tasks can be assigned to each cluster or processor. The ordering of a system into a bordered block upper triangular form facilitates a reasonable large-grain partitioning. A new algorithm which produces this fo...

A parallel row projection solver for large sparse linear systems

In this paper we present a parallel iterative solver for large and sparse nonsymmetric linear systems. The solver is based on a row-projection algorithm, derived from the symmetrized block version of the Kacz-marz method with Conjugate Gradient acceleration. A comparison with some h'rylov subspace methods shows the remarkable robustness of this algorithm when applied to systems with eingevalues...