An efficient implementation of parallel eigenvalue computation for massively parallel processing

摘要

This paper describes an efficient implementation and evaluation of a parallel eigensolver for computing all eigenvalues of dense symmetric matrices. Our eigensolver uses a Householder tridiagonalization method, which has higher parallelism and performance than conventional methods when problem size is relatively small, e.g., the order of 10,000. This is very important for relevant practical applications, where many diagonalizations for such matrices are required so often. The routine was evaluated to the ScaLAPACK library on 1024 processors of thf HITACHI SR2201.