Several manufacturers have already started to commercialize near-bank Processing-In-
Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures …

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 …

Several manufacturers have already started to commercialize near-bank Processing-In-
Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures …

Sparse general matrix-matrix multiplication (SpGEMM) is one of the key kernels of
preconditioners such as algebraic multigrid method or graph algorithms. However, the …

The sparse matrix–vector product (SpMV), considered one of the seven dwarfs (numerical
methods of significance), is essential in high-performance real-world scientific and analytical …

Sparse matrix–vector multiplication (SpMV) is one of the most indispensable kernels of
solving problems in numerous applications, but its performance of SpMV is limited by the …

Sparse Matrix-Vector multiplication (SpMV) is one of the most significant yet challenging
issues in computational science area. It is a memory-bound application whose performance …

This paper presents an efficient weighted least squares (WLS) distributed algorithm for multi-
area power system state estimation including measurements provided by the supervisory …

It is extremely challenging to run controlled studies comparing multiple augmented reality
(AR) systems. We use an ldquoAR simulationrdquo approach, in which a virtual reality (VR) …

The Sparse Matrix-Vector Multiplication (SpMV) kernel is used in a broad class of linear
algebra computations. SpMV computations result in a performance bottleneck in many high …