Inspired by the success of blocking to improve the performance of algorithms for sparse matrix vector multiplication [5] and sparse direct factorization [1], we explore the benefits of blocking in related sparse graph algorithms. A natural question is whether the benefits of local blocking extend to other sparse graph algorithms. Here we examine algorithms for finding a maximum-weight complete ...

We address the recovery of sparse vectors in an overcomplete, linear and noisy multiple measurement framework, where the measurement matrix is known upto a permutation of its rows. We derive sparse Bayesian learning (SBL) based updates for joint recovery of the unknown sparse vectors and the sensing order, represented using a permutation matrix. We model the sparse vectors using multiple uncorr...

Many scientific applications involve operations on sparse matrices. However, due to irregularities induced by the sparsity patterns, many operations on sparse matrices execute inefficiently on traditional scalar and vector architectures. To tackle this problem a scheme has been proposed consisting of two parts: (a) An extension to a vector architecture to support sparse matrix-vector multiplica...