Massively parallel grid generation on HPC systems

Lintermann, Andreas; Schlimpert, Stephan; Grimmen, Jerry H.; Günther, Claudia; Meinke, Matthias; Schröder, Wolfgang

doi:10.1016/j.cma.2014.04.009

Cited by 93 publications

(32 citation statements)

References 26 publications

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“…2.1. Since the generation of a parallel geometry is based on the parallel grid generator presented in [14], an introduction to the grid generator is given in Sec. 2.2.…”

Section: Methodsmentioning

confidence: 99%

“…The parallel grid generator [14] enables a fully automatic creation of large-scale computational Cartesian meshes stored in a hierarchical octree in a short amount of time on hundreds of thousand of processes. For process communication it makes use of the Message Passing Interface (MPI) and I/O is performed by the parallel NetCDF library [25].…”

Section: Parallel Grid Generationmentioning

confidence: 99%

“…The output of the grid generator enables to run simulations on a quasi-arbitrary number of processes and is only limited by the number of elements in L and by the available memory per MPI-rank. For further details on the meshing algorithm and its parallel performance, the reader is referred to [14].…”

Section: Inmentioning

confidence: 99%

“…However, they necessitate the geometry for the often time consuming manual construction of the computational mesh. In contrast, the generation of hierarchical Cartesian meshes can be executed completely automatically in a short amount of time [12,13,14]. Such meshes are well suited for complex geometries and are used for a variety of applications [2,3,6,15,16,17,18] in which the computational grids are generated from a geometrical surface representation given in STereo Lithography (STL) format.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, a new algorithm to generate a parallel geometry from a STL file during the mesh generation process is presented. It is based on the hierarchical Cartesian mesh generator introduced in [14] and avoids wasting memory by distributing the geometry corresponding to the applied domain decomposition. This not only reduces the total and local amount of memory occupied by the geometry, but also the time of pre-simulation processing.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Efficient Parallel Geometry Distribution for the Simulation of Complex Flows

Lintermann¹

2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

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Abstract. Highly resolved intrinsic geometrical shapes used in three-dimensional parallel simulations of fluid flows consume a large portion of the available memory when loaded serially on every process. This demands for a memory efficient implementation of a distributed geometry which is however a non-trivial task when complex spatial domain decomposition methods for the flow domain are involved. To overcome this problem, an algorithm to generate a parallel geometry during the mesh generation is proposed that enables a low-memory subdivision of the geometry based on the decomposition of the flow field. The applied meshing method generates computational grids that can be used for simulations on a quasi-arbitrary number of cores on which the geometry is distributed in an efficient preprocessing step. This allows reducing the number of instances of the geometry in the global memory of the simulation to about one. The algorithm is used to generate a parallel geometry for a large shape consisting of 7 · 10 6 triangles, i.e., for a geometry representing the whole respiratory tract down to the 12 th lung generation. For this case, performance and memory consumption measurements are given for simulations on 8,192 up to 131,072 cores and juxtaposed to results obtained from simulations using non-parallel geometries. The findings show that with the new method not only the memory usage could be reduced by the factors of 1,802 and 19,936 for core numbers of 8,192 and 131,072 but also a large speed-up factor of about 51 is obtained in the geometry I/O and preprocessing. Furthermore, the parallel geometry allows using the sweet spot with respect to a combination of distributed and shared memory parallelization leading to an increase of the computational speed of about 1.43.

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“…2.1. Since the generation of a parallel geometry is based on the parallel grid generator presented in [14], an introduction to the grid generator is given in Sec. 2.2.…”

Section: Methodsmentioning

confidence: 99%

Section: Parallel Grid Generationmentioning

confidence: 99%

Section: Inmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Efficient Parallel Geometry Distribution for the Simulation of Complex Flows

Lintermann¹

2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

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A large‐scale parallel hybrid grid generation technique for realistic complex geometry

Zhao

Zhang

et al. 2020

Numerical Methods in Fluids

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Summary High‐Performance Computing (HPC) systems and Computational Fluid Dynamics (CFD) have made significant progress in recent years; however, as the basis of the large‐scale parallel computing, the massive grid generation of billions of cells has become a bottleneck problem. In this study, a parallel grid generation technique is proposed to generate large‐scale mixed grids with arbitrary cell types and scales. The basic idea of our method is analogous to the global mesh refinement technique. An initial coarse grid with arbitrary cell types is regarded as a background mesh which is partitioned into subzones, and subzones are assigned onto different CPU cores. After the cells and faces in each subzone are split, the inserted new points of the solid wall are projected onto the original CAD entities to preserve the geometry accurately. Finally, the tangled cells caused by the projection in the boundary layer are untangled by a local Radial Basis Function mesh deformation technique. Furthermore, a parallel partition approach and an efficient wall distance computing technique for massive grids are developed also to shorten the preprocessing time. The tests show that the preprocessing efficiency has been increased by two or three orders compared with traditional methods. Billions of grids are generated for the AIAA JSM high‐lift model and the Chinese CHN‐T1 transport model to test the ability of the parallel grid generation technique. The maximum scale up to 19 billion mixed elements is generated using 16 384 CPU cores in parallel, and the mesh quality is acceptable for CFD simulations.

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An efficient numerical method for fully‐resolved particle simulations on high‐performance computers

Schneiders

Grimmen

Meinke

et al. 2015

Proc Appl Math and Mech

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A numerical method for fully-resolved particle simulations on adaptively-refined Cartesian meshes is presented. When the particle size is on the order of or smaller than the smallest scales of the carrier flow, fully resolving the particle surfaces on uniform Cartesian meshes results in a tremendous computational effort. This is circumvented by locally refining the mesh near the moving particle surfaces. A new dynamic load-balancing strategy is described which enables the application of this approach to particulate turbulent flows on high-performance computers.

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Massively parallel grid generation on HPC systems

Cited by 93 publications

References 26 publications

Efficient Parallel Geometry Distribution for the Simulation of Complex Flows

Efficient Parallel Geometry Distribution for the Simulation of Complex Flows

A large‐scale parallel hybrid grid generation technique for realistic complex geometry

An efficient numerical method for fully‐resolved particle simulations on high‐performance computers

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