In this paper, we analyze the main features and discuss the tuning of the algorithms for the
direct solution of sparse linear systems on distributed memory computers developed in the …

In many large scale scientific or engineering computations, ranging from computing the
frequency response of a circuit to the earthquake response of a buildingto the energy levels …

We present the main algorithmic features in the software package SuperLU_DIST, a
distributed-memory sparse direct solver for large sets of linear equations. We give in detail …

Solving large sparse symmetric positive definite systems of linear equations is a crucial and
time-consuming step, arising in many scientific and engineering applications. The block …

The standard LU factorization-based solution process for linear systems can be enhanced in
speed or accuracy by employing mixed-precision iterative refinement. Most recent work has …

This paper provides a comprehensive study and comparison of two state-of-the-art direct
solvers for large sparse sets of linear equations on large-scale distributed-memory …

Minimum degree and nested dissection are the two most popular reordering schemes used
to reduce fill‐in and operation count when factoring and solving sparse matrices. Most of the …

We present and analyze a general algorithm which computes an efficient static scheduling
of block computations for a parallel LDL t factorization of sparse symmetric positive definite …

In this paper, we present the developments realized in the Ouragan project around the
parallelization of a Matlab-like tool called Scilab. These developments use high …

Since the last decade, most of the supercomputer architectures are based on clusters of
SMP nodes. In those architectures the exchanges between processors are made through …