154,515 research outputs found
FullSWOF_Paral: Comparison of two parallelization strategies (MPI and SKELGIS) on a software designed for hydrology applications
In this paper, we perform a comparison of two approaches for the
parallelization of an existing, free software, FullSWOF 2D (http://www.
univ-orleans.fr/mapmo/soft/FullSWOF/ that solves shallow water equations for
applications in hydrology) based on a domain decomposition strategy. The first
approach is based on the classical MPI library while the second approach uses
Parallel Algorithmic Skeletons and more precisely a library named SkelGIS
(Skeletons for Geographical Information Systems). The first results presented
in this article show that the two approaches are similar in terms of
performance and scalability. The two implementation strategies are however very
different and we discuss the advantages of each one.Comment: 27 page
Memetic Multilevel Hypergraph Partitioning
Hypergraph partitioning has a wide range of important applications such as
VLSI design or scientific computing. With focus on solution quality, we develop
the first multilevel memetic algorithm to tackle the problem. Key components of
our contribution are new effective multilevel recombination and mutation
operations that provide a large amount of diversity. We perform a wide range of
experiments on a benchmark set containing instances from application areas such
VLSI, SAT solving, social networks, and scientific computing. Compared to the
state-of-the-art hypergraph partitioning tools hMetis, PaToH, and KaHyPar, our
new algorithm computes the best result on almost all instances
A scalable parallel finite element framework for growing geometries. Application to metal additive manufacturing
This work introduces an innovative parallel, fully-distributed finite element
framework for growing geometries and its application to metal additive
manufacturing. It is well-known that virtual part design and qualification in
additive manufacturing requires highly-accurate multiscale and multiphysics
analyses. Only high performance computing tools are able to handle such
complexity in time frames compatible with time-to-market. However, efficiency,
without loss of accuracy, has rarely held the centre stage in the numerical
community. Here, in contrast, the framework is designed to adequately exploit
the resources of high-end distributed-memory machines. It is grounded on three
building blocks: (1) Hierarchical adaptive mesh refinement with octree-based
meshes; (2) a parallel strategy to model the growth of the geometry; (3)
state-of-the-art parallel iterative linear solvers. Computational experiments
consider the heat transfer analysis at the part scale of the printing process
by powder-bed technologies. After verification against a 3D benchmark, a
strong-scaling analysis assesses performance and identifies major sources of
parallel overhead. A third numerical example examines the efficiency and
robustness of (2) in a curved 3D shape. Unprecedented parallelism and
scalability were achieved in this work. Hence, this framework contributes to
take on higher complexity and/or accuracy, not only of part-scale simulations
of metal or polymer additive manufacturing, but also in welding, sedimentation,
atherosclerosis, or any other physical problem where the physical domain of
interest grows in time
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