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 …

Tiling is a key technique for data locality optimization and is widely used in high-
performance implementations of dense matrix-matrix multiplication for multicore/manycore …

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

The symmetric sparse matrix-vector multiplication (SymmSpMV) is an important building
block for many numerical linear algebra kernel operations or graph traversal applications …

Sparse matrix-vector multiplication (SpMV) is an important kernel in many scientific
applications and is known to be memory bandwidth limited. On modern processors with …

Sparse general matrix-matrix multiplication (SpGEMM) is one of the most fundamental
building blocks in sparse linear solvers, graph processing frameworks and machine learning …

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

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 …