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

Sparse matrix-vector multiplication (SpMV) is a crucial computing kernel with widespread
applications in iterative algorithms. Over the past decades, research on SpMV optimization …

Important workloads, such as machine learning and graph analytics applications, heavily
involve sparse linear algebra operations. These operations use sparse matrix compression …

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 …

We study compression techniques for parallel in-memory graph algorithms, and show that
we can achieve reduced space usage while obtaining competitive or improved performance …

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 …

SpMV is a key linear algebra algorithm and has been widely used in many important
application domains. As a result, numerous attempts have been made to optimize SpMV on …

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 …

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 important kernel in both traditional high
performance computing and emerging data-intensive applications. By far, SpMV libraries …