Core Ideas The numerical solution of Richards' equation remains challenging. Space/time
discretization affects both computational effort and accuracy. Adaption of space and time …

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 …

The FEniCS Project is a collaborative project for the development of innovative concepts
and tools for automated scientific computing, with a particular focus on the solution of …

This book presents the fundamentals of sparse matrix algorithms, from theory to algorithms
and data structures to working code. The focus is on direct methods for solving systems of …

We consider the problem of designing a dynamic scheduling strategy that takes into account
both workload and memory information in the context of the parallel multifrontal factorization …

CHOLMOD is a set of routines for factorizing sparse symmetric positive definite matrices of
the form A or AA T, updating/downdating a sparse Cholesky factorization, solving linear …

Several manufacturers have already started to commercialize near-bank Processing-In-
Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures …

A non-linear MHD code, named JOREK, is under development with the aim of studying the
non-linear evolution of the MHD instabilities thought to be responsible for edge localized …

Several manufacturers have already started to commercialize near-bank Processing-In-
Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures …

Important workloads, such as machine learning and graph analytics applications, heavily
involve sparse linear algebra operations. These operations use sparse matrix compression …