Sparse matrix computations are pivotal to advancing high-performance scientific applications, particularly as modern numerical simulations and data analyses demand efficient management of large, ...

This paper presents an overview of the multifrontal method for the solution of large sparse symmetric positive definite linear systems. The method is formulated in terms of frontal matrices, update ...

Abstract: Sparse-matrix dense-matrix multiplication (SpMM) receives one sparse matrix and one dense matrix as two inputs, and outputs one dense matrix as a result. It plays a vital role in various ...

Sparse matrix computations are prevalent in many scientific and technical applications. In many simulation applications, the solving of the sparse matrix-vector multiplication (SpMV) is critical for ...

“Several manufacturers have already started to commercialize near-bank Processing-In-Memory (PIM) architectures. Near-bank PIM architectures place simple cores close to DRAM banks and can yield ...

In 1989 I was pleased and honored to be awarded the ORSA/TIMS (now INFORMS) John von Neumann Theory Prize for my work in portfolio theory, sparse matrices, and SIMSCRIPT. The following is a ...

The classic sparse matrix screen based on Jancaric and Kim (1991) and modified by Cudney et al (1994). Samples salts, polymers, organics and pH (see conditions). Helsinki Random II A combined sparse ...

In our work on DIPY, we have encountered a new warning when upgrading to 1.7.1: dipy/dipy#3606. Since the conversion to a sparse matrix was not occurring before, we wonder whether it is needlessly ...