Distributed high-performance parallel mesh generation with ViennaMesh (original) (raw)
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Effective large scale computing software for parallel mesh generation
2011
Scientists commonly turn to supercomputers or Clusters of Workstations with hundreds (even thousands) of nodes to generate meshes for large-scale simulations. Parallel mesh generation software is then used to decompose the original mesh generation problem into smaller sub-problems that can be solved (meshed) in parallel. The size of the final mesh is limited by the amount of aggregate memory of the parallel machine. Also, requesting many compute nodes on a shared computing resource may result in a long waiting, far surpassing the time it takes to solve the problem. These two problems (i.e., insufficient memory when computing on a small number of nodes, and long waiting times when using many nodes from a shared computing resource) can be addressed by using out-of-core algorithms. These are algorithms that keep most of the dataset out-of-core (i.e., outside of memory, on disk) and load only a portion in-core (i.e., into memory) at a time. We explored two approaches to out-of-core comp...
Scalable system for large unstructured mesh simulation
2012
Dealing with large simulation is a growing challenge. Ideally for the wellparallelized software prepared for high performance, the problem solving capability depends on the available hardware resources. But in practice there are several technical details which reduce the scalability of the system and prevent the effective use of such a software for large problems. In this work we describe solutions implemented in order to obtain a scalable system to solve and visualize large scale problems. The present work is based on Kratos MutliPhysics [1] framework in combination with GiD [2] pre and post processor. The applied techniques are verified by CFD simulation and visualization of a wind tunnel problem with more than 100 millions of elements in our in-hose cluster in CIMNE.
From Mesh Generation to Scientific Visualization: An End-to-End Approach to Parallel Supercomputing
2006
Parallel supercomputing has traditionally focused on the inner kernel of scientific simulations: the solver. The front and back ends of the simulation pipeline - problem description and interpretation of the output - have taken a back seat to the solver when it comes to attention paid to scalability and performance, and are often relegated to offline, sequential computation. As the largest simulations move beyond the realm of the terascale and into the petascale, this decomposition in tasks and platforms becomes increasingly untenable. We propose an end-to-end approach in which all simulation components - meshing, partitioning, solver, and visualization - are tightly coupled and execute in parallel with shared data structures and no intermediate I/O. We present our implementation of this new approach in the context of octree-based finite element simulation of earthquake ground motion. Performance evaluation on up to 2048 processors demonstrates the ability of the end-to-end approach to overcome the scalability bottlenecks of the traditional approach
Parallel unstructured mesh generation by an advancing front method
Mathematics and Computers in Simulation, 2007
Mesh generation is a critical step in high fidelity computational simulations. High-quality and high-density meshes are required to accurately capture the complex physical phenomena. A robust approach for a parallel framework has been developed to generate large-scale meshes in a short period of time. A coarse tetrahedral mesh is generated first to provide the basis of block interfaces and then is partitioned into a number of sub-domains using METIS partitioning algorithms. A volume mesh is generated on each sub-domain in parallel using an advancing front method. Dynamic load balancing is achieved by evenly distributing work among the processors. All the sub-domains are combined to create a single volume mesh. The combined volume mesh can be smoothed to remove the artifacts in the interfaces between sub-domains. A void region is defined inside each sub-domain to reduce the data points during the smoothing operation. The scalability of the parallel mesh generation is evaluated to quantify the improvement on shared-and distributed-memory computer systems.
The meshing framework ViennaMesh for finite element applications
Journal of Computational and Applied Mathematics, 2014
The applicability of the meshing framework ViennaMesh for finite element simulations is investigated. Meshing tools are highly diverse, meaning that each software package offers specific properties, such as the conforming Delaunay property. The feasibility of these properties tends to be domain specific, thus restricting the general application of a meshing tool. For research purposes, it is desirable to have a rich toolset consisting of the various meshing packages in order to be able to quickly apply the various packages to the problem at hand. Different meshing tools have to be utilized to support a broader range of mesh properties. Further contributing to this problem is the lack of a common programming interface, impeding convenient switching of meshing backends. ViennaMesh tackles this challenge by providing a uniform meshing interface and reusable mesh-related tools, like CGAL, Gmsh, Netgen, and Tetgen. We depict the feasibility of our approach by discussing two applications relevant to finite element simulations, being a local mesh optimization and an adaptive mesh refinement application.
Lecture Notes in Computational Science and Engineering
Parallel mesh generation is a relatively new research area between the boundaries of two scientific computing disciplines: computational geometry and parallel computing. In this chapter we present a survey of parallel unstructured mesh generation methods. Parallel mesh generation methods decompose the original mesh generation problem into smaller subproblems which are meshed in parallel. We organize the parallel mesh generation methods in terms of two basic attributes: (1) the sequential technique used for meshing the individual subproblems and (2) the degree of coupling between the subproblems. This survey shows that without compromising in the stability of parallel mesh generation methods it is possible to develop parallel meshing software using off-the-shelf sequential meshing codes. However, more research is required for the efficient use of the state-of-the-art codes which can scale from emerging chip multiprocessors (CMPs) to clusters built from CMPs.
International Journal of High Performance Computing Applications, 2000
Realizing scalable performance on high performance computing systems is not straightforward for single-phenomenon codes (such as computational fluid dynamics [CFD]). This task is magnified considerably when the target software involves the interactions of a range of phenomena that have distinctive solution procedures involving different discretization methods. The problems of addressing the key issues of retaining data integrity and the ordering of the calculation procedures are significant. A strategy for parallelizing this multiphysics family of codes is described for software exploiting finite-volume discretization methods on unstructured meshes using iterative solution procedures. A mesh partitioning-based SPMD approach is used. However, since different variables use distinct discretization schemes, this means that distinct partitions are required; techniques for addressing this issue are described using the mesh-partitioning tool, JOSTLE. In this contribution, the strategy is tested for a variety of test cases under a wide range of conditions (e.g., problem size, number of processors, asynchronous/synchronous communications, etc.) using a variety of strategies for mapping the mesh partition onto the processor topology.
Meshing Strategies for Large Scale Problems
Large scale simulation is moving towards sustained teraflop rates. This simulation power potentiates the most cutting edge large-scale resources to solve large and challenging problems in science and engineering. For finite element simulations, a significant challenge is how to generate meshes with billions of nodes and elements and to deliver such meshes to processors of such large-scale systems. In this work, we discuss some strategies ranging from parametric mesh generators, suitable for simple geometries, to general approaches using octree based meshes for immersed boundary geometries.
Parallel programming environment for mesh generation
2002
This paper presents a parallel programming environment for mesh generation. Our approach is based on overdecomposition. The programming environment supports: low-latency one-sided communication, global address space in the context of data/object mobility, automatic message forwarding and dynamic load balancing. These are the minimum requirements for developing efficient adaptive parallel mesh generation codes on distributed memory machines and clusters of workstations and PCs. Performance data from a 3-dimensional advancing front mesh generation code designed for crack propagation simulations suggest that the flexibility and general nature of our parallel programming environment does not cause undue overhead.
Extreme-Scale Parallel Mesh Generation: Telescopic Approach
2015
In this poster we focus and present our preliminary results pertinent to the integration of multiple parallel Delaunay mesh generation methods into a coherent hierarchical framework. The goal of this project is to study our telescopic approach and to develop Delaunay-based methods to explore concurrency at all hardware layers using abstractions at (a) medium-grain level for many cores within a single chip and (b) coarse-grain level, i.e., sub-domain level using proper error metricand application-specific continuous decomposition methods. © 2015 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of organizing committee of the 24th International Meshing Roundtable (IMR24).