Summary Continuously indexed Gaussian fields (GFs) are the most important ingredient in
spatial statistical modelling and geostatistics. The specification through the covariance …

Distributed processing of large-scale graph data has many practical applications and has
been widely studied. In recent years, a lot of distributed graph processing frameworks and …

In mammalian brains, millions to billions of cells form complex interaction networks to enable
a wide range of functions. The enormous diversity and intricate organization of cells have …

Full-batch training on Graph Neural Networks (GNN) to learn the structure of large graphs is
a critical problem that needs to scale to hundreds of compute nodes to be feasible. It is …

In the emerging field of multi-physics simulations, we often face the challenge to establish
new connections between physical fields, to add additional aspects to existing models, or to …

The purpose of this text is to offer an overview of the most popular domain decomposition
methods for partial differential equations (PDEs). The presentation is kept as much as …

Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics-a
very active area of research in which few up-to-date reference works are available. This is …

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 …

The subject of sparse matrices has its root in such diverse fields as management science,
power systems analysis, surveying, circuit theory, and structural analysis. Efficient use of …

WOMBAT is a software package for quantitative genetic analyses of continuous traits, fitting
a linear, mixed model; estimates of covariance components and the resulting genetic …