Combinatorial techniques have become essential tools across the landscape of
computational science, and some of the combinatorial ideas undergirding these tools are …

Many sparse matrix computations can be speeded up if the matrix is first reordered.
Reordering was originally developed for direct methods but it has recently become popular …

Conjugate gradient (CG) and biconjugate gradient stabilized (BiCGSTAB) are effective
methods used for solving sparse linear systems. We in this paper propose Mille-feuille, a …

This paper is concerned with applying bandwidth and profile reduction reordering
algorithms prior to computing an incomplete Cholesky factorization and using this as a …

In this work, we apply the adaptive discontinuous Galerkin (DGAFEM) method to the
convection dominated non-linear, quasi-stationary diffusion convection reaction equations …

The solution of large sparse linear systems is often the most time-consuming part of many
science and engineering applications. Computational fluid dynamics, circuit simulation …

The eigenvector corresponding to the second smallest eigenvalue of the Laplacian of a
graph, known as the Fiedler vector, has a number of applications in areas that include matrix …

In this work we investigate the numerical difficulties that arise in optimizing the efficiency of
Newtonian fluids simulations on a massively parallel computing hardware like a GPU. In …

Many simulations in science and engineering give rise to sparse linear systems of
equations. It is a well known fact that the cost of the simulation process is almost always …

With availability of large‐scale parallel platforms comprised of tens‐of‐thousands of
processors and beyond, there is significant impetus for the development of scalable parallel …