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 …

Black-box topology optimization (BBTO) uses evolutionary algorithms and other soft
computing techniques to generate near-optimal topologies of mechanical structures …

This paper proposes a new level set-based topology optimization (TO) method using a
parallel strategy of Graphics Processing Units (GPUs) and the isogeometric analysis (IGA) …

Matrix-free solvers for finite element method (FEM) avoid assembly of elemental matrices
and replace sparse matrix-vector multiplication required in iterative solution method by an …

Sparse matrix-vector (SpMV) multiplication is a vital building block for numerous scientific
and engineering applications. This paper proposes SURAA (translates to speed in arabic), a …

Exploiting matrix symmetry to halve memory footprint offers an opportunity for accelerating
memory-bound computations like Sparse Matrix-Vector Multiplication (SpMV). However …

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

Iterative solutions of sparse linear systems and sparse eigenvalue problems have a
fundamental role in vital fields of scientific research and engineering. The crucial computing …

As the presence of finite element implementations on General Purpose Graphics Processing
Units (GPGPUs) is the literature increases, detailed and in-breadth testing of the hardware is …

Abstract The Sparse Matrix-Vector (SpMV) product is a key operation used in many scientific
applications. This work proposes a new sparse matrix storage scheme, the AXT format, that …