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

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 …

General sparse matrix-sparse matrix multiplication (SpGEMM) is one of the fundamental
linear operations in a wide variety of scientific applications. To implement efficient SpGEMM …

This work presents a systematic exploration on the promise and special challenges of deep
learning for sparse matrix format selection---a problem of determining the best storage …

The Sunway TaihuLight, equipped with 10 million cores, is currently the world's third fastest
supercomputer. SpMV is one of core algorithms in many high-performance computing …

Sparse Matrix-Vector multiplication (SpMV) is an essential computational kernel in many
application scenarios. Tens of sparse matrix formats and implementations have been …

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 …