Everyone who has seen the movie Wall Street will have seen a commercial security policy in
action. The recent work of Clark and Wilson and the WIPCIS initiative (the Workshop on …

The performance of sparse matrix vector multiplication (SpMV) is important to computational
scientists. Compressed sparse row (CSR) is the most frequently used format to store sparse …

Sparse matrix-vector multiplication (SpMV) is a crucial computing kernel with widespread
applications in iterative algorithms. Over the past decades, research on SpMV optimization …

The multiplication of a sparse matrix by a dense vector (SpMV) is a centerpiece of scientific
computing applications: it is the essential kernel for the solution of sparse linear systems and …

When authors present new storage formats for sparse matrices, they usually focus mainly on
a single evaluation criterion, which is the performance of sparse matrix-vector multiplication …

The Sunway TaihuLight, equipped with 10 million cores, is currently the world's third fastest
supercomputer. SpMV is one of core algorithms in many high-performance computing …

Sparse matrix-vector multiplication (SpMV) is one of the important kernels of many iterative
algorithms for solving sparse linear systems. The limited storage and computational …

Sparse matrix-vector multiplication (SpMV) is a fundamental building block for various
numerical computing applications. However, most existing GPU-SpMV approaches may …

Sparse matrix vector multiplication (SpMV) is an important linear algebra primitive. Recent
research has focused on improving the performance of SpMV on GPUs when using …

Sparse linear algebra is fundamental to numerous areas of applied mathematics, science
and engineering. In this paper, we propose an efficient data structure named AdELL+ for …