BoxLib contains all the functionality needed to write a parallel, block-structured AMR application. The fundamental parallel abstraction is the MultiFab, which holds the data on the union of grids at a level. A MultiFab is composed of FAB’s; each FAB is an array of data on a single grid. During each MultiFab operation the FAB’s composing that MultiFab are distributed among the cores. MultiFab’s at each level of refinement are distributed independently. The software supports two data distribution schemes, as well as a dynamic switching scheme that decides which approach to use based on the number of grids at a level and the number of processors. The first scheme is based on a heuristic knapsack algorithm; the second is based on the use of a Morton-ordering space-filling curve. MultiFab operations are performed with an owner computes rule with each processor operating independently on its local data. For operations that require data owned by other processors, the MultiFab operations are preceded by a data exchange between processors. Each processor contains meta-data that is needed to fully specify the geometry and processor assignments of the MultiFab’s. At a minimum, this requires the storage of an array of boxes specifying the index space region for each AMR level of refinement. The meta-data can thus be used to dynamically evaluate the necessary communication patterns for sharing data amongst processors, enabling us to optimize communications patterns within the algorithm. One of the advantages of computing with fewer, larger grids in the hybrid OpenMP--MPI approach (see below) is that the size of the meta-data is substantially reduced.

References in zbMATH (referenced in 20 articles )

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  1. Chung, Joseph D.; Zhang, Xiao; Kaplan, Carolyn R.; Oran, Elaine S.: The barely implicit correction algorithm for low-Mach-number flows. II: Application to reactive flows (2020)
  2. Muralidharan, Balaji; Menon, Suresh: A consistent multi-level subgrid scale closure for large eddy simulation of compressible flow using adaptive mesh refinement (2019)
  3. Pei, Chaoxu; Vahab, Mehdi; Sussman, Mark; Hussaini, M. Yousuff: A hierarchical space-time spectral element and moment-of-fluid method for improved capturing of vortical structures in incompressible multi-phase/multi-material flows (2019)
  4. Motheau, Emmanuel; Duarte, Max; Almgren, Ann; Bell, John B.: A hybrid adaptive low-Mach number/compressible method: Euler equations (2018)
  5. Muralidharan, Balaji; Menon, Suresh: Simulation of moving boundaries interacting with compressible reacting flows using a second-order adaptive Cartesian cut-cell method (2018)
  6. Schornbaum, Florian; Rüde, Ulrich: Extreme-scale block-structured adaptive mesh refinement (2018)
  7. Zhang, Xiao; Chung, Joseph D.; Kaplan, Carolyn R.; Oran, Elaine S.: The barely implicit correction algorithm for low-Mach-number flows (2018)
  8. Donna Calhoun, Carsten Burstedde: ForestClaw: A parallel algorithm for patch-based adaptive mesh refinement on a forest of quadtrees (2017) arXiv
  9. Jannis Teunissen, Ute Ebert: Afivo: a framework for quadtree/octree AMR with shared-memory parallelization and geometric multigrid methods (2017) arXiv
  10. May, Sandra; Berger, Marsha: An explicit implicit scheme for cut cells in embedded boundary meshes (2017)
  11. Berzins, Martin; Beckvermit, Jacqueline; Harman, Todd; Bezdjian, Andrew; Humphrey, Alan; Meng, Qingyu; Schmidt, John; Wight, Charles: Extending the Uintah framework through the petascale modeling of detonation in arrays of high explosive devices (2016)
  12. Muralidharan, Balaji; Menon, Suresh: A high-order adaptive Cartesian cut-cell method for simulation of compressible viscous flow over immersed bodies (2016)
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