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 …

In this paper we focus on common data reorganization operations such as shuffle,
pack/unpack, swap, transpose, and layout transformations. Although these operations …

When authors present new storage formats for sparse matrices, they usually focus mainly on
a single evaluation criterion, which is the performance of sparse matrix-vector multiplication …

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

Dense and sparse tensors allow the representation of most bulk data structures in
computational science applications. We show that sparse tensor algebra can also be used …

Shared-memory systems such as regular desktops now possess enough memory to store
large data. However, the training process for data classification can still be slow if we do not …

Sparse matrix-vector and matrix-transpose-vector multiplication (SpMM TV) repeatedly
performed as z← AT x and y← A z (or y A w) for the same sparse matrix A is a kernel …

Sparse basic linear algebra subprograms (BLAS) are fundamental building blocks for
numerous scientific computations and graph applications. Compared with Dense BLAS …

Direct and iterative methods are often used to solve linear systems in engineering. The
matrices involved can be large, which leads to heavy computations on the central …

Many high performance computing applications require computing both sparse matrix‐
vector product (SMVP) and sparse matrix‐transpose vector product (SMTVP) for better …