Sparse matrix–vector multiplication (SpMV) of the form y = Ax is a widely used computational kernel existing in many scientific applications. The input matrix A is sparse. The input vector x …

Nov 3, 2023 · Sparse matrix vector multiplication (SpMV) is a core computational kernel of nearly every implicit sparse linear algebra solver. The performance of algorithms ranging from simple …

Sparse Matrix-Vector Multiplication refers to a fundamental computational operation used in scientific and engineering applications that involves multiplying a sparse matrix with a vector.

We begin by highlighting two representative applications of SpMV, then conduct an in-depth overview of the important techniques that optimize SpMV on modern architectures, which we …

Sparse matrix-vector multiplication (SpMV) is arguably the most important operation in sparse matrix computations. Iterative methods for solving large linear systems (Ax = b) and …

Jan 9, 2017 · 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 …

In this work, we examine sparse matrix-vector multiply (SpMV) – one of the most heavily used kernels in scientific computing – across a broad spec-trum of multicore designs.

To ensure convergence the absolute value of the largest Eigenvalue of G (the spectral radius of G) needs to be strictly lower than one, i.e, G needs to be contractive. where D is the diagonal …

Sparse Matrix Vector multiplication (SpMV) is a fundamental operation in various computational science applications, characterized by a significant degree of in

Nov 3, 2023 · Sparse matrix vector multiplication (SpMV) is a core computational kernel of nearly every implicit sparse linear algebra solver. The performance of algorithms ranging from simple …