Using hybrid parallelism to improve memory use in the Uintah framework

  • Authors:
  • Qingyu Meng;Martin Berzins;John Schmidt

  • Affiliations:
  • University of Utah, Salt lake City, UT;University of Utah, Salt lake City, UT;University of Utah, Salt lake City, UT

  • Venue:
  • Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery
  • Year:
  • 2011

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Abstract

The Uintah Software framework was developed to provide an environment for solving fluid-structure interaction problems on structured adaptive grids on large-scale, long-running, data-intensive problems. Uintah uses a combination of fluid-flow solvers and particle-based methods for solids together with a novel asynchronous task-based approach with fully automated load balancing. Uintah's memory use associated with ghost cells and global meta-data has become a barrier to scalability beyond O(100K) cores. A hybrid memory approach that addresses this issue is described and evaluated. The new approach based on a combination of Pthreads and MPI is shown to greatly reduce memory usage as predicted by a simple theoretical model, with comparable CPU performance.