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— In this document, we present a parallel implementation in FreeFem++ of scal-able two-level domain decomposition methods. Numerical studies with highly heterogeneous problems are then performed on large clusters in order to assert the performance of our code.
The 3400 species of Eumolpinae constitute one of the largest subfamilies of leaf beetles (Chrysomelidae). Their systematics is still largely based on late 19th century monographs and remains highly unsatisfactory. Only recently, some plesiomorphic lineages have been split out as separate subfamilies, including the southern hemisphere Spilopyrinae and the(More)
Domain decomposition methods are, alongside multigrid methods, one of the dominant paradigms in contemporary large-scale partial differential equation simulation. In this paper, a lightweight implementation of a theoretically and numerically scalable preconditioner is presented in the context of overlapping methods. The performance of this work is assessed(More)
—Network interface cards are one of the key components to achieve efficient parallel performance. In the past, they have gained new functionalities such as lossless transmission and remote direct memory access that are now ubiquitous in high-performance systems. Prototypes of next generation network cards now offer new features that facilitate device(More)
The conditional correlation patterns of an anatomical shape may provide some important information on the structure of this shape. We propose to investigate these patterns by Gaussian Graphical Modelling. We design a model which takes into account both local and long-distance dependencies. We provide an algorithm which estimates sparse long-distance(More)
The motivation of this work is the detection of cerebrovascular accidents by microwave tomographic imaging. This requires the solution of an inverse problem relying on a minimization algorithm (for example, gradient-based), where successive iterations consist in repeated solutions of a direct problem. The reconstruction algorithm is extremely(More)
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