Aug 2, 2011 · We propose a new parallelization technique for Lanzos type al- gorithms for solving sparse linear systems over finite fields on mesh cluster.

In this paper we report on an implementation of a parallel algorithm to minimize PLA realizations of logic functions. The algorithm is derived from a widely ...

The Lanczos algorithm is most commonly used in approximating a small number of extreme eigenvalues and eigenvectors for symmetric large sparse matrices.

Parallelization may allow us to solve much larger problems as we can divide the work among many machines. The structure of the Block Lanczos algorithm suggests.

Two of the commonly used versions of the Lanczos method for eigenvalues problems are the shift-and-invert Lanczos method and the restarted Lanczos method.

Abstract. In this paper, we report our parallel implementations of the. Lanczos sparse linear system solving algorithm over large prime fields,.

I. INTRODUCTION. Singular Value Decomposition (SVD) is an important classical technique which is used for many pur- poses [8].

This was achieved using a method for purifying the Lanczos vectors (i.e. by repeatedly reorthogonalizing each newly generated vector with all previously ...

The speed-up shown at the top of Figure 8 shows that our parallelization based on collective communication is indeed very efficient. Even for a system of 24 ...

we can draw the conclusion that for massively parallel systems the inner products may be the slowest of the three operation types in the Lanczos method. The ...