A survey of high level frameworks in block-structured adaptive mesh refinement packages

Dubey, Anshu; Almgren, Ann S.; Bell, John B.; Berzins, Martin; Brandt, Steve; Bryan, Greg L.; Colella, Phillip; Graves, Daniel; Lijewski, Michael J.; Löffler, Frank; O’Shea, Brian W.; Schnetter, Erik; Straalen, Brian Van; Weide, Klaus

doi:10.1016/j.jpdc.2014.07.001

Cited by 134 publications

(93 citation statements)

References 63 publications

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“…The capability to adaptively alter the domain partitioning is particularly useful for many engineering applications and thus a commonly sought feature in frameworks for numerical simulations [32]. When working on Eulerian grids, it allows maintaining a fine resolution wherever necessary, while at the same time coarsening the grid in less interesting regions, which often drastically reduces the computational cost and memory consumption of the simulation.…”

Section: Adaptive Refinement and Load Balancingmentioning

confidence: 99%

waLBerla: A block-structured high-performance framework for multiphysics simulations

Bauer

Eibl

Godenschwager

et al. 2021

Computers & Mathematics with Applications

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Programming current supercomputers efficiently is a challenging task. Multiple levels of parallelism on the core, on the compute node, and between nodes need to be exploited to make full use of the system. Heterogeneous hardware architectures with accelerators further complicate the development process. waLBerla addresses these challenges by providing the user with highly efficient building blocks for developing simulations on block-structured grids. The block-structured domain partitioning is flexible enough to handle complex geometries, while the structured grid within each block allows for highly efficient implementations of stencil-based algorithms. We present several example applications realized with waLBerla, ranging from lattice Boltzmann methods to rigid particle simulations. Most importantly, these methods can be coupled together, enabling multiphysics simulations. The framework uses meta-programming techniques to generate highly efficient code for CPUs and GPUs from a symbolic method formulation. To ensure software quality and performance portability, a continuous integration toolchain automatically runs an extensive test suite encompassing multiple compilers, hardware architectures, and software configurations.

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Section: Adaptive Refinement and Load Balancingmentioning

confidence: 99%

waLBerla: A block-structured high-performance framework for multiphysics simulations

Bauer

Eibl

Godenschwager

et al. 2021

Computers & Mathematics with Applications

View full text Add to dashboard Cite

show abstract

“…For example, a common concern for the PDE solvers is the data structure containing multiple field components that have identical spatial layout. Should it be an array with an added dimension for the field components or a structure; and within the array or structure, what should be the order for storing in memory for performance [17], [18]. There is no one best layout for every platform.…”

Section: Data Structuresmentioning

confidence: 99%

Trends in Data Locality Abstractions for HPC Systems

Unat

Dubey

Hoefler

et al. 2017

IEEE Trans. Parallel Distrib. Syst.

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Abstract-The cost of data movement has always been an important concern in high performance computing (HPC) systems. It has now become the dominant factor in terms of both energy consumption and performance. Support for expression of data locality has been explored in the past, but those efforts have had only modest success in being adopted in HPC applications for various reasons. However, with the increasing complexity of the memory hierarchy and higher parallelism in emerging HPC systems, locality management has acquired a new urgency. Developers can no longer limit themselves to low-level solutions and ignore the potential for productivity and performance portability obtained by using locality abstractions. Fortunately, the trend emerging in recent literature on the topic alleviates many of the concerns that got in the way of their adoption by application developers. Data locality abstractions are available in the forms of libraries, data structures, languages and runtime systems; a common theme is increasing productivity without sacrificing performance. This paper examines these trends and identifies commonalities that can combine various locality concepts to develop a comprehensive approach to expressing and managing data locality on future large-scale high-performance computing systems.

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“…The amr consists in coarsening the cells containing only a few atoms, using criteria such as the number of particles or average density. A large amount of amr software have been developed [13] for various physical problems such as astrophysics with Enzo [7] or BoxLib [22], Chombo [8] to solve PDE.…”

Section: Adaptive Mesh Refinement (Amr)mentioning

confidence: 99%

Combining Task-based Parallelism and Adaptive Mesh Refinement Techniques in Molecular Dynamics Simulations

Prat

Colombet

Namyst

2018

Proceedings of the 47th International Conference on Parallel Processing

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Modern parallel architectures require applications to generate massive parallelism so as to feed their large number of cores and their wide vector units. We revisit the extensively studied classical Molecular Dynamics N-body problem in the light of these hardware constraints. We use Adaptive Mesh Refinement techniques to store particles in memory, and to optimize the force computation loop using multi-threading and vectorization-friendly data structures. Our design is guided by the need for load balancing and adaptivity raised by highly dynamic particle sets, as typically observed in simulations of strong shocks resulting in material micro-jetting. We analyze performance results on several simulation scenarios, over nodes equipped by Intel Xeon Phi Knights Landing (KNL) or Intel Xeon Skylake (SKL) processors. Performance obtained with our OpenMP implementation outperforms state-of-the-art implementations (LAMMPS) on both steady and micro-jetting particles simulations. In the latter case, our implementation is 4.7 times faster on KNL, and 2 times faster on SKL.

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A survey of high level frameworks in block-structured adaptive mesh refinement packages

Cited by 134 publications

References 63 publications

waLBerla: A block-structured high-performance framework for multiphysics simulations

waLBerla: A block-structured high-performance framework for multiphysics simulations

Trends in Data Locality Abstractions for HPC Systems

Combining Task-based Parallelism and Adaptive Mesh Refinement Techniques in Molecular Dynamics Simulations

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