8. Parallel Adaptive Mesh Refinement

Diachin, Lori Freitag; Hornung, Richard D.; Plassmann, Paul E.; Wissink, Andy

doi:10.1137/1.9780898718133.ch8

Cited by 17 publications

(13 citation statements)

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“…These generally fall into two categories, structured (SAMR) and unstructured (UAMR) (see the review in [17] and the references therein). SAMR methods represent the PDE solution on a composite of hierarchical, adaptivelygenerated, logically-rectangular, structured grids.…”

Section: The Alps Library For Parallel Amrmentioning

confidence: 99%

Scalable adaptive mantle convection simulation on petascale supercomputers

Burstedde

Ghattas

Gurnis

et al. 2008

2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis

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Abstract-Mantle convection is the principal control on the thermal and geological evolution of the Earth. Mantle convection modeling involves solution of the mass, momentum, and energy equations for a viscous, creeping, incompressible non-Newtonian fluid at high Rayleigh and Peclet numbers. Our goal is to conduct global mantle convection simulations that can resolve faulted plate boundaries, down to 1 km scales. However, uniform resolution at these scales would result in meshes with a trillion elements, which would elude even sustained petaflops supercomputers. Thus parallel adaptive mesh refinement and coarsening (AMR) is essential.We present RHEA, a new generation mantle convection code designed to scale to hundreds of thousands of cores. RHEA is built on ALPS, a parallel octree-based adaptive mesh finite element library that provides new distributed data structures and parallel algorithms for dynamic coarsening, refinement, rebalancing, and repartitioning of the mesh. ALPS currently supports low order continuous Lagrange elements, and arbitrary order discontinuous Galerkin spectral elements, on octree meshes. A forest-ofoctrees implementation permits nearly arbitrary geometries to be accommodated. Using TACC's 579 teraflops Ranger supercomputer, we demonstrate excellent weak and strong scalability of parallel AMR on up to 62,464 cores for problems with up to 12.4 billion elements. With RHEA's adaptive capabilities, we have been able to reduce the number of elements by over three orders of magnitude, thus enabling us to simulate large-scale mantle convection with finest local resolution of 1.5 km.

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Section: The Alps Library For Parallel Amrmentioning

confidence: 99%

Scalable adaptive mantle convection simulation on petascale supercomputers

Burstedde

Ghattas

Gurnis

et al. 2008

2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis

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“…Many works exist on parallel mesh processing and adaptive hexahedral mesh refinement (see for example [31][32][33][34][35][36][37][38][39][40][41][42]) which shows the interest of this field of research. Several libraries are available that provide efficient parallel adaptive mesh refinement, for example libMesh [43] or PUMI [44].…”

Section: A Parallel Algorithm Of Mesh Processing Based On 3-dmapsmentioning

confidence: 99%

Distributed Combinatorial Maps for Parallel Mesh Processing

et al. 2018

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Abstract:We propose a new strategy for the parallelization of mesh processing algorithms. Our main contribution is the definition of distributed combinatorial maps (called n-dmaps), which allow us to represent the topology of big meshes by splitting them into independent parts. Our mathematical definition ensures the global consistency of the meshes at their interfaces. Thus, an n-dmap can be used to represent a mesh, to traverse it, or to modify it by using different mesh processing algorithms. Moreover, an nD mesh with a huge number of elements can be considered, which is not possible with a sequential approach and a regular data structure. We illustrate the interest of our solution by presenting a parallel adaptive subdivision method of a 3D hexahedral mesh, implemented in a distributed version. We report space and time performance results that show the interest of our approach for parallel processing of huge meshes.

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“…Diachin et al [23] discuss a number of techniques for implementing SAMR and UAMR (i.e. Unstructured Adaptive Mesh Refinement) in parallel, but do not mention any parallelism present within the algorithm itself.…”

Section: Related Workmentioning

confidence: 99%

A new parallelization scheme for adaptive mesh refinement

Löffler

Cao

Brandt

et al. 2016

Journal of Computational Science

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a b s t r a c tWe present a new method for parallelization of adaptive mesh refinement called Concurrent Structured Adaptive Mesh Refinement (CSAMR). This new method offers the lower computational cost (i.e. wall time × processor count) of subcycling in time, but with the runtime performance (i.e. smaller wall time) of evolving all levels at once using the time step of the finest level (which does more work than subcycling but has less parallelism). We demonstrate our algorithm's effectiveness using an adaptive mesh refinement code, AMSS-NCKU, and show performance on Blue Waters and other high performance clusters. For the class of problem considered in this paper, our algorithm achieves a speedup of 1.7-1.9 when the processor count for a given AMR run is doubled, consistent with our theoretical predictions.

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8. Parallel Adaptive Mesh Refinement

Cited by 17 publications

References 0 publications

Scalable adaptive mantle convection simulation on petascale supercomputers

Scalable adaptive mantle convection simulation on petascale supercomputers

Distributed Combinatorial Maps for Parallel Mesh Processing

A new parallelization scheme for adaptive mesh refinement

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