Quantum random-access memory (QRAM) is a mechanism to access data (quantum or
classical) based on addresses which are themselves a quantum state. QRAM has a long …

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 …

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

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

Graphics processing units (GPUs) have delivered a remarkable performance for a variety of
high performance computing (HPC) applications through massive parallelism. One such …

Canonical polyadic decomposition (CPD) is one of the most common tensor computations
adopted in many scientific applications. The major bottleneck of CPD is matricized tensor …

Heterogeneous platforms composed of multiple different types of computing devices (such
as CPUs, GPUs, and Intel MICs) have been widely used recently. However, most of parallel …

The irregular distribution of non-zero elements of large-scale sparse matrix leads to low data
access efficiency caused by the unique architecture of the Sunway many-core processor …

The Sparse General Matrix-Matrix multiplication (SpGEMM) is a fundamental component for
many applications, such as algebraic multigrid methods (AMG), graphic processing, and …