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 …

This work is an overview of our preliminary experience in developing a high-performance
iterative linear solver accelerated by GPU coprocessors. Our goal is to illustrate the …

This paper presents a unique method of performance analysis and optimization for sparse
matrix-vector multiplication (SpMV) on GPU. This method has wide adaptability for different …

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 …

This paper presents a sparse matrix partitioning strategy to improve the performance of
SpMV on GPUs and multicore CPUs. This method has wide adaptability for different types of …

Sparse Matrix-Vector multiplication (SpMV) is an essential computational kernel in many
application scenarios. Tens of sparse matrix formats and implementations have been …

We present graphics processing unit (GPU) data structures and algorithms to efficiently
solve sparse linear systems that are typically required in simulations of multi‐body systems …

Sparse matrix–vector multiplication (SpMV) is an important issue in scientific computing and
engineering applications. The performance of SpMV can be improved using parallel …