This paper introduces a storage format for sparse matrices, called compressed sparse
blocks (CSB), which allows both Ax and A, x to be computed efficiently in parallel, where A is …

We improve the performance of sparse matrix-vector multiplication (SpMV) on modern cache-
based superscalar machines when the matrix structure consists of multiple, irregularly …

We present a strictly balanced method for the parallel computation of sparse matrix-vector
products (SpMV). Our algorithm operates directly upon the Compressed Sparse Row (CSR) …

We present a perfectly balanced," merge-based" parallel method for computing sparse
matrix-vector products (SpMV). Our algorithm operates directly upon the Compressed …

Sparse matrix-vector multiplication (SpMV) is of singular importance in sparse linear
algebra. In contrast to the uniform regularity of dense linear algebra, sparse operations …

We design and develop a work-efficient multithreaded algorithm for sparse matrix-sparse
vector multiplication (SpMSpV) where the matrix, the input vector, and the output vector are …

In this paper we present a new technique for sparse matrix multiplication on vector
multiprocessors based on the efficient implementation of a segmented sum operation. We …

Sparse matrix-vector multiplication (SpMV) is an important kernel in many scientific
applications and is known to be memory bandwidth limited. On modern processors with …

A new method is presented for distributing data in sparse matrix-vector multiplication. The
method is two-dimensional, tries to minimize the true communication volume, and also tries …

Sparse matrix-vector multiplication (SpMxV) is one of the most important computational
kernels in scientific computing. It often suffers from poor cache utilization and extra load …