In the past decade, Graphics Processing Units have played an important role in the field of
high-performance computing and they still advance new fields such as IoT, autonomous …

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 …

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 …

Sparse matrix-vector multiplication (SpMV) is a widely used computational kernel. The most
commonly used format for a sparse matrix is CSR (Compressed Sparse Row), but a number …

Sparse matrix-vector multiplication (SpMV) is a core kernel in numerous applications,
ranging from physics simulation and large-scale solvers to data analytics. Many GPU …

Initially introduced as special-purpose accelerators for graphics applications, graphics
processing units (GPUs) have now emerged as general purpose computing platforms for a …

Sparse matrix-vector multiplication (SpMV) is one of the key operations in linear algebra.
Overcoming thread divergence, load imbalance and non-coalesced and indirect memory …

Compressed sparse row (CSR) is a frequently used format for sparse matrix storage.
However, the state-of-the-art CSR-based sparse matrix-vector multiplication (SpMV) …

Sparse matrix-vector multiplication (SpMV) is a central building block for scientific software
and graph applications. Recently, heterogeneous processors composed of different types of …