1,647 research outputs found
Large-Scale Simulations of Clusters of Galaxies
We discuss some of the computational challenges encountered in simulating the
evolution of clusters of galaxies. Eulerian adaptive mesh refinement (AMR)
techniques can successfully address these challenges but are currently being
used by only a few groups. We describe our publicly available AMR code, FLASH,
which uses an object-oriented framework to manage its AMR library, physics
modules, and automated verification. We outline the development of the FLASH
framework to include collisionless particles, permitting it to be used for
cluster simulation.Comment: 3 pages, 3 figures, to appear in Proceedings of the VII International
Workshop on Advanced Computing and Analysis Techniques in Physics Research
(ACAT 2000), Fermilab, Oct. 16-20, 200
Dynamic Load Balancing Techniques for Particulate Flow Simulations
Parallel multiphysics simulations often suffer from load imbalances
originating from the applied coupling of algorithms with spatially and
temporally varying workloads. It is thus desirable to minimize these imbalances
to reduce the time to solution and to better utilize the available hardware
resources. Taking particulate flows as an illustrating example application, we
present and evaluate load balancing techniques that tackle this challenging
task. This involves a load estimation step in which the currently generated
workload is predicted. We describe in detail how such a workload estimator can
be developed. In a second step, load distribution strategies like space-filling
curves or graph partitioning are applied to dynamically distribute the load
among the available processes. To compare and analyze their performance, we
employ these techniques to a benchmark scenario and observe a reduction of the
load imbalances by almost a factor of four. This results in a decrease of the
overall runtime by 14% for space-filling curves
OOFEM — an Object-oriented Simulation Tool for Advanced Modeling of Materials and Structures
The aim of this paper is to describe the object-oriented design of the finite element based simulation code. The overall, object-oriented structure is described, and the role of the fundamental classes is discussed. The paper discusses the advanced parallel, adaptive, and multiphysics capabilities of the OOFEM code, and illustrates them on the basis of selected examples
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
Research and Education in Computational Science and Engineering
Over the past two decades the field of computational science and engineering
(CSE) has penetrated both basic and applied research in academia, industry, and
laboratories to advance discovery, optimize systems, support decision-makers,
and educate the scientific and engineering workforce. Informed by centuries of
theory and experiment, CSE performs computational experiments to answer
questions that neither theory nor experiment alone is equipped to answer. CSE
provides scientists and engineers of all persuasions with algorithmic
inventions and software systems that transcend disciplines and scales. Carried
on a wave of digital technology, CSE brings the power of parallelism to bear on
troves of data. Mathematics-based advanced computing has become a prevalent
means of discovery and innovation in essentially all areas of science,
engineering, technology, and society; and the CSE community is at the core of
this transformation. However, a combination of disruptive
developments---including the architectural complexity of extreme-scale
computing, the data revolution that engulfs the planet, and the specialization
required to follow the applications to new frontiers---is redefining the scope
and reach of the CSE endeavor. This report describes the rapid expansion of CSE
and the challenges to sustaining its bold advances. The report also presents
strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie
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