The Sparse Matrix-Vector multiplication (SpMV) kernel scales poorly on shared memory
systems with multiple processing units due to the streaming nature of its data access pattern …

Previous research work has identified memory bandwidth as the main bottleneck of the
ubiquitous Sparse Matrix-Vector Multiplication kernel. To attack this problem, we aim at …

In this paper, we revisit the performance issues of the widely used sparse matrix-vector
multiplication (SpMxV) kernel on modern microarchitectures. Previous scientific work reports …

The limiting factor for efficiency of sparse linear solvers is the memory bandwidth. In this
work, we describe a fast Conjugate Gradient solver for unstructured problems, which runs on …

In this paper we revisit the performance issues of the widely used sparse matrix-vector
multiplication (SpMxV) kernel on modern microarchitectures. Previous scientific work reports …

This paper presents a low-overhead optimizer for the ubiquitous sparse matrix-vector
multiplication (SpMV) kernel. Architectural diversity among different processors together with …

Achieving peak performance on sparse operations is challenging. The distribution of the non-
zero elements and underlying hardware platform affect the execution efficiency. Given the …

Many real-world computations involve sparse data structures in the form of sparse matrices.
A common strategy for optimizing sparse matrix operations is to reorder a matrix to improve …

Modern neural network architectures are becoming larger and deeper, with increasing
computational resources needed for training and inference. One approach toward handling …

The sparse matrix-vector multiplication kernel exhibits limited potential for taking advantage
of modern shared memory architectures due to its large memory bandwidth requirements …