Linear algebra algorithms in a heterogeneous cluster of personal computers

Abstract

Cluster computing is presently a major research area, mostly for high performance computing. The work presented refers to the application of cluster computing in a small scale where a virtual machine is composed of a small number of off-the-self-personal computers connected by a low cost network. A methodology to determine the optimal number of processors to be used in a computation is presented as well as the speedup results obtained for the matrix-matrix multiplication and for the symmetric QR algorithm for eigenvector computation which are significant building blocks for applications in the target image processing and analysis domain. The load balancing strategy is also addressed.