Cluster Optimization and Parallelization of Simulations with Dynamically Adaptive Grids

  • Martin Schreiber
  • Tobias Weinzierl
  • Hans-Joachim Bungartz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8097)

Abstract

The present paper studies solvers for partial differential equations that work on dynamically adaptive grids stemming from spacetrees. Due to the underlying tree formalism, such grids efficiently can be decomposed into connected grid regions (clusters) on-the-fly. A graph on those clusters classified according to their grid invariancy, workload, multi-core affinity, and further meta data represents the inter-cluster communication. While stationary clusters already can be handled more efficiently than their dynamic counterparts, we propose to treat them as atomic grid entities and introduce a skip mechanism that allows the grid traversal to omit those regions completely. The communication graph ensures that the cluster data nevertheless are kept consistent, and several shared memory parallelization strategies are feasible. A hyperbolic benchmark that has to remesh selected mesh regions iteratively to preserve conforming tessellations acts as benchmark for the present work. We discuss runtime improvements resulting from the skip mechanism and the implications on shared memory performance and load balancing.

Keywords

dynamic adaptivity cluster skipping shared memory load balancing space-filling curve 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Martin Schreiber
    • 1
  • Tobias Weinzierl
    • 1
  • Hans-Joachim Bungartz
    • 1
  1. 1.Technische Universität MünchenGarchingGermany

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