Parallel adaptive simulations on unstructured meshes

Shephard, Mark S.; Jansen, Kenneth E.; Sahni, Onkar; Diachin, Lori Freitag

doi:10.1088/1742-6596/78/1/012053

Cited by 23 publications

(27 citation statements)

References 18 publications

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“…42,58,125,134,135 Xie, Seol, and Shephard 136 present the generic programming paradigm built on two decades of research into parallel adaptive data structures and partitioning. 42,59,[137][138][139][140][141] This allows the adaptive grid algorithm to navigate the large associative data structures that change topology as grid elements are added, removed, or modified. Adaptive grid schemes must be capable of supporting large relative motion by adapting the grid 142 or operating on the component grids of an overset grid system.…”

Section: 54mentioning

confidence: 99%

Unstructured Grid Adaptation: Status, Potential Impacts, and Recommended Investments Towards CFD 2030

Park

Krakos

Michal

et al. 2016

46th AIAA Fluid Dynamics Conference

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Unstructured grid adaptation is a powerful tool to control Computational Fluid Dynamics (CFD) discretization error. It has enabled key increases in the accuracy, automation, and capacity of some fluid simulation applications. Slotnick et al. provide a number of case studies in the CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences to illustrate the current state of CFD capability and capacity. The study authors forecast the potential impact of emerging High Performance Computing (HPC) environments forecast in the year 2030 and identify that mesh generation and adaptivity will continue to be significant bottlenecks in the CFD workflow. These bottlenecks may persist because very little government investment has been targeted in these areas. To motivate investment, the impacts of improved grid adaptation technologies are identified. The CFD Vision 2030 Study roadmap and anticipated capabilities in complementary disciplines are quoted to provide context for the progress made in grid adaptation in the past fifteen years, current status, and a forecast for the next fifteen years with recommended investments. These investments are specific to mesh adaptation and impact other aspects of the CFD process. Finally, a strategy is identified to di↵use grid adaptation technology into production CFD work flows.

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Section: 54mentioning

confidence: 99%

Unstructured Grid Adaptation: Status, Potential Impacts, and Recommended Investments Towards CFD 2030

Park

Krakos

Michal

et al. 2016

46th AIAA Fluid Dynamics Conference

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show abstract

“…In addition to the CFD flow solver PHASTA, adaptive meshing [8,9,11,10] and mesh partitioning [7] techniques are other essential ingredients required to generate and partition significantly large (in the order of 5 billion or more elements) 3D unstructured finite element meshes for the target applications. Indeed, the application of reliable numerical simulations requires them to be executed in an automated manner with explicit control of the approximations made.…”

Section: Adaptive Mesh Control and Mesh Partitioningmentioning

confidence: 99%

Petascale, Adaptive CFD (ALCF ESP Technical Report): ALCF-2 Early Science Program Technical Report

Jansen¹,

Rasquin²

2013

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“…Consider four different 16-way partitionings of a 1,103,018-element mesh used in a simulation of blood flow in a human aorta [30]. Here, the geometric method recursive inertial bisection (RIB) [28] and/or the multilevel graph partitioner in ParMetis [20] are used for partitioning. Only two partition quality factors are considered: computational balance, and two surface index measures, although other factors [5] should be considered.…”

Section: Partitioning and Dynamic Load Balancingmentioning

confidence: 99%

Hierarchical Partitioning and Dynamic Load Balancing for Scientific Computation

Teresco

Faik

Flaherty

2006

Applied Parallel Computing. State of the Art in Scientific Computing

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Abstract. Cluster and grid computing has made hierarchical and heterogeneous computing systems increasingly common as target environments for large-scale scientific computation. A cluster may consist of a network of multiprocessors. A grid computation may involve communication across slow interfaces. Modern supercomputers are often large clusters with hierarchical network structures. For maximum efficiency, software must adapt to the computing environment. We focus on partitioning and dynamic load balancing, in particular on hierarchical procedures implemented within the Zoltan Toolkit, guided by DRUM, the Dynamic Resource Utilization Model. Here, different balancing procedures are used in different parts of the domain. Preliminary results show that hierarchical partitionings are competitive with the best traditional methods on a small hierarchical cluster.Modern three-dimensional scientific computations must execute in parallel to achieve acceptable performance. Target parallel environments range from clusters of workstations to the largest tightly-coupled supercomputers. Hierarchical and heterogeneous systems are increasingly common as symmetric multiprocessing (SMP) nodes are combined to form the relatively small clusters found in many institutions as well as many of today's most powerful supercomputers. Network hierarchies arise as grid technologies make Internet execution more likely and modern supercomputers are built using hierarchical interconnection networks. MPI implementations may exhibit very different performance characteristics depending on the underlying network and message passing implementation (e.g., [32]). Software efficiency may be improved using optimizations based on system characteristics and domain knowledge. Some have accounted for clusters of SMPs by using a hybrid programming model, with message passing for internode communication and multithreading for intra-node communication (e.g., [1,27]), with varying degress of success, but always with an increased burden on programmers to program both levels of parallelization.Our focus has been on resource-aware partitioning and dynamic load balancing, achieved by adjusting target partition sizes or the choice of a dynamic

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Parallel adaptive simulations on unstructured meshes

Cited by 23 publications

References 18 publications

Unstructured Grid Adaptation: Status, Potential Impacts, and Recommended Investments Towards CFD 2030

Unstructured Grid Adaptation: Status, Potential Impacts, and Recommended Investments Towards CFD 2030

Petascale, Adaptive CFD (ALCF ESP Technical Report): ALCF-2 Early Science Program Technical Report

Hierarchical Partitioning and Dynamic Load Balancing for Scientific Computation

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