Abstract
Both massively parallel computers and clusters of workstations are considered promising platforms for numerical scientific computing. This paper describes the first distributed-memory implementation of the split-merge algorithm, an eigenvalue solver for symmetric tridiagonal matrices that uses Laguerre's iteration and exploits the separation property in order to create independent subtasks. Implementations of the split-merge algorithm on both an nCUBE-2 hypercube and a cluster of Sun Spare-10 workstations are described, with emphasis on load balancing, communication overhead, and interaction with other user processes. A performance study demonstrates the advantage of the new algorithm over a parallelization of the well-known bisection algorithm. A comparison of the performance of the nCUBE-2 and cluster implementations supports the claim that workstation clusters offer a cost-effective alternative to massively parallel computers for certain scientific applications.
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