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

Sparse matrix-vector multiplication (SpMV) plays a key role in computational science and
engineering, graph processing, and machine learning applications. Much work on SpMV …

Sparse linear algebra is crucial in many application domains, but challenging to handle
efficiently in both software and hardware, with one-and two-sided operand sparsity handled …

Sparse matrix-vector multiplication (SpMV) is a fundamental routine in computational
science and engineering. Its optimization methods on various homogeneous parallel …

Sparse Matrix-Vector Multiplication (SpMV) is an essential sparse kernel. Numerous
methods have been developed to accelerate SpMV. However, no single method consistently …

This paper presents a novel approach to optimize multiple invocations of a sparse matrix-
vector multiplication (SpMV) kernel performed on the same sparse matrix A and dense …

The SpMV kernel is characterized by high performance variation per input matrix and
computing platform. While GPUs were considered State-of-the-Art for SpMV, with the …

In recent years, many researchers have been using CPU for quantum computing simulation.
However, in reality, the simulation efficiency of the large‐scale simulator is low on a single …

Sparse matrix-vector multiplication (SpMV) plays a pivotal role in large-scale scientific
computing. Despite the increasing use of low-power multicore digital signal processors …

Link analysis is a fundamental task for graph analytics, as it enables the identification of
important nodes and patterns in the graph. Link analysis algorithms typically require …