Sparse matrix-vector multiplication (SpMV) is an important primitive across a wide range of
application domains such as scientific computing and graph analytics. Due to its intrinsic …

With the extensive use of GPUs in modern supercomputers, accelerating sparse matrix-
vector multiplication (SpMV) on GPUs received much attention in the last couple of decades …

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 …

SpMV (Sparse matrix–vector multiplication) is widely used in many fields. Improving the
performance of SpMV has been the pursuit of many researchers. Parallel SpMV using multi …

Tensor algebra is widely used in many applications, such as scientific computing, machine
learning, and data analytics. The tensors represented real-world data are usually large and …

Modern real-world application scenarios like Internet services consist of a diversity of AI and
non-AI modules with huge code sizes and long and complicated execution paths, which …

Blocking is an important optimization option available to mitigate the data movement
overhead and improve the temporal locality in SpMV, a sparse BLAS kernel with irregular …

Sparse Matrix-Vector Multiplication (SpMV) plays an important role in many scientific and
industry applications, and remains a well-known challenge due to the high sparsity and …

Sparse-dense linear algebra is crucial in many domains, but challenging to handle
efficiently on CPUs, GPUs, and accelerators alike; multiplications with sparse formats like …

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 …