Iterative methods are currently the solvers of choice for large sparse linear systems of
equations. However, it is well known that the key factor for accelerating, or even allowing for …

Many GPU applications perform data transfers to and from GPU memory at regular intervals.
For example because the data does not fit into GPU memory or because of internode …

Graph partitioning is important in distributing workloads on parallel compute systems,
computing sparse matrix re-orderings, and designing VLSI circuits. Refinement algorithms …

An adaptive algorithm is presented to generate automatically the nonzero pattern of the
block factored sparse approximate inverse (BFSAI) preconditioner. It is demonstrated that in …

A novel parallel preconditioner for symmetric positive definite matrices is developed
coupling a generalized factored sparse approximate inverse (FSAI) with an incomplete LU …

In this paper, an octree pattern-based massively parallel preconditioned conjugate gradient
(PCG) solver is developed for large-scale elastostatic and implicit elastodynamic …

Contact mechanics can be addressed numerically by finite elements using either a penalty
formulation or the Lagrange multipliers. The penalty approach leads to a linearized …

Half-precision hardware support is now almost ubiquitous. In contrast to its active use in AI,
half-precision is less commonly employed in scientific and engineering computing. The …

Adaptive block factorized sparse approximate inverse (FSAI)(ABF) is a novel algebraic
preconditioner for the cost-effective parallel solution of symmetric positive definite linear …

The repeated solution in time of the linear system arising from the finite element integration
of coupled consolidation equations is a major computational effort. This system can be …