Sparse-sparse matrix multiplication (SpGEMM) is a computation kernel widely used in
numerous application domains such as data analytics, graph processing, and scientific …

Generalized Sparse Matrix-Matrix Multiplication (SpGEMM) is a ubiquitous task in various
engineering and scientific applications. However, inner product based SpGEMM introduces …

Sparse matrices are widely used in graph and data analytics, machine learning, engineering
and scientific applications. This paper describes and analyzes OuterSPACE, an accelerator …

Sparse matrix-sparse matrix multiplication (spMspM) is at the heart of a wide range of
scientific and machine learning applications. spMspM is inefficient on general-purpose …

Recently, a number of researchers have investigated a class of graph partitioning algorithms
that reduce the size of the graph by collapsing vertices and edges, partition the smaller …

Linear programming has been the dominant paradigm in optimization since Dantzig's
development of the simplex method in the 1940s. In 1984, the publication of a paper by …

The purpose of this work is to present the MOSEK optimizer intended for solution of large-
scale sparse linear programs. The optimizer is based on the homogeneous interior-point …

Recently, a number of researchers have investigated a class of algorithms that are based on
multilevel graph partitioning that have moderate computational complexity, and provide …

In this paper we present experiments with a class of graph partitioning algorithms that
reduce the size of the graph by collapsing vertices and edges, partition the smaller graph …

In this paper, we describe scalable parallel algorithms for symmetric sparse matrix
factorization, analyze their performance and scalability, and present experimental results for …