Leapfrog variants of iterative methods for linear algebraic equations
Journal of Computational and Applied Mathematics - Special issue on iterative methods for the solution of linear systems
Multiplication of matrices of arbitrary shape on a data parallel computer
Parallel Computing
Performance of a parallel matrix multiplication routine on Intel iPSC/860
Parallel Computing
Parallel sparse QR factorization on shared memory architectures
Parallel Computing
Implementation of QR up- and downdating on a massively parallel computer
Parallel Computing
Applied numerical linear algebra
Applied numerical linear algebra
A class of Lanczos-like algorithms implemented on parallel computers
Parallel Computing
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In order to use parallel computers in specific applications, algorithms need to be developed and mapped onto parallel computer architectures. Main memory access for shared memory system or global communication in message passing system deteriorate the computation speed. In this paper, it is found that the m-step generalization of the block Lanczos method enhances parallel properties by forming m simultaneous search direction vector blocks. QR factorization, which lowers the speed on parallel computers, is not necessary in the m-step block Lanczos method. The m-step method has the minimized synchronization points, which resulted in the minimized global communications and main memory accesses compared to the standard method.