Applying parallel computer systems to solve symmetric tridiagonal eigenvalue problems

A block parallel partitioning method for computing the eigenvalues of symmetric tridiagonal matrix is presented. The algorithm is based on partitioning, in a way that ensures load balance during computation. This method is applicable to both shared memory- and distributed memory-MIMD systems. Compared with other parallel tridiagonal eigenvalue algorithms existing in the literature, the proposed algorithm achieves a higher speedup of O(p) on a parallel computer with p-fold parallelism, which is linear, and the data communication between processors is less than that required for other methods. The results were tested and evaluated on an MIMD machine, and were within 62% to 98% of the predicted performance.