Wilkinson defined a sparse matrix as one with enough zeros that it pays to take advantage of
them. 1 This informal yet practical definition captures the essence of the goal of direct …

Hardware trends over the last decade show increasing complexity and heterogeneity in high
performance computing architectures, which presents developers of CFD applications with …

Large sparse linear systems of equations are ubiquitous in science, engineering and
beyond. This open access monograph focuses on factorization algorithms for solving such …

Abstract Graphic Processing Units (GPUs) have greatly exceeded their initial role of
graphics accelerators and have taken a new role of co-processors for computation—heavy …

Many problems in engineering and scientific computing require the solution of a large
number of small systems of linear equations. Due to their high processing power, Graphics …

In recent years, there has been considerable interest in the potential for graphics processing
units (GPUs) to speed up the performance of sparse direct linear solvers. Efforts have …

Many numerical simulation methods, such as finite-element analysis, were originally
formulated to run serially or in parallel on central processing units (CPUs). However …

To face the advent of multicore processors and the ever increasing complexity of hardware
architectures, programming models based on DAG parallelism regained popularity in the …

In this paper we present computational cost estimates for parallel shared memory
isogeometric multi-frontal solvers. The estimates show that the ideal isogeometric shared …

We present a parallel sparse direct solver for multicore architectures based on Directed
Acyclic Graph (DAG) scheduling. Recently, DAG scheduling has become popular in …