Several manufacturers have already started to commercialize near-bank Processing-In-
Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures …

Sparse linear algebra includes the fundamental and important operations in various large-
scale scientific computing and real-world applications. There exists performance bottleneck …

Several manufacturers have already started to commercialize near-bank Processing-In-
Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures …

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) plays a key role in computational science and
engineering, graph processing, and machine learning applications. Much work on SpMV …

Dependence between iterations in sparse computations causes inefficient use of memory
and computation resources. This paper proposes sparse fusion, a technique that generates …

Sparse matrix vector multiplication (SpMV) is an important computational kernel in traditional
high-performance computing and emerging data-intensive applications. Previous SpMV …

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 …

Multi-dimensional, large-scale, and sparse data, which can be neatly represented by sparse
tensors, are increasingly used in various applications such as data analysis and machine …

In most computational codes, the core computational kernel is the Sparse Matrix-Vector
product (SpMV) that enables specialized linear algebra libraries like PETSc to be used …