Parallel studies of the invariant subspace decomposition approach for banded symmetric matrices
Christian H Bischof, Steven Huss‐Lederman, Xiaobai Sun, Anna Tsao, Thomas Turnbull · 1995
We present an overview of the banded Invariant Subspace Decomposition Algorithm for symmetric matrices and describe a parallel implementation of this algorithm. The algorithm described here is a promising variant of the Invariant Subspace Decomposition Algorithm for dense symmetric matrices (SYISDA) that retains the property of using scalable primitives, while requiring significantly less overall computation than SYISDA. 1 Introduction Computation of eigenvalues and eigenvectors is an essential kernel in many applications, and several promising parallel algorithms have been investigated. The work presented in this paper is part of the PRISM (Parallel Research on Invariant Subspace Methods) Project, which involves researchers from Argonne National Laboratory, the Supercomputing Research Center, the University of California at Berkeley, and the University of Kentucky. The goal of the PRISM project is the development of algorithms and software for solving large-scale eigenvalue problems ...