Dependence between iterations in sparse computations causes inefficient use of memory
and computation resources. This paper proposes sparse fusion, a technique that generates …

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

In this paper, we present WACO, a novel method of co-optimizing the format and the
schedule of a given sparsity pattern in a sparse tensor program. A core challenge in this …

Sparse Matrix-Vector Multiplication (SpMV) is a fundamental operation in a number of
scientific and engineering problems. When the sparse matrices processed are large …

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

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 …

Structural dynamics simulation plays an important role in research on reactor design and
complex engineering. The Hybrid Total Finite Element Tearing and Interconnecting (HTFETI) …

Multigrid algorithms are widely used to solve large-scale sparse linear systems, which is
essential for many high-performance workloads. The symmetric Gauss-Seidel (SYMGS) …

Sparse matrices are an integral part of scientific simulations. As hardware evolves new
sparse matrix storage formats are proposed aiming to exploit optimizations specific to the …