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 an essential kernel in sparse linear algebra
and has been studied extensively on all modern processor and accelerator architectures …

SpMV (Sparse matrix-vector multiplication) has attracted the attention of researchers in
related fields at home and abroad. Of course, improving SpMV performance has also been a …

SpMV (Sparse matrix-vector multiplication) is an essential component in scientific computing
and has attracted the attention of researchers in related fields at home and abroad. With the …

Sparse matrix-vector multiplication (SpMV) is an important kernel in many scientific
applications and is known to be memory bandwidth limited. On modern processors with …

Sparse matrix-vector multiplication (SpMV) is a fundamental building block for numerous
applications. In this paper, we propose CSR5 (Compressed Sparse Row 5), a new storage …

Sparse matrix-vector multiplication (spMVM) is the most time-consuming kernel in many
numerical algorithms and has been studied extensively on all modern processor and …

The execution time of many scientific computing applications is dominated by the time spent
in performing sparse matrix vector multiplication (SMV; y← A· x). We consider improving the …

Sparse matrix-vector multiplication (SpMV) has always been a hot topic of research for
scientific computing and big data processing, but the sparsity and discontinuity of the …

Optimizing sparse matrix–vector multiplication (SpMV) is challenging due to the non-uniform
distribution of the non-zero elements of the sparse matrix. The best-performing SpMV format …