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Tools to support mesh adaptation on massively parallel computers

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Abstract

The scalable execution of parallel adaptive analyses requires the application of dynamic load balancing to repartition the mesh into a set of parts with balanced work load and minimal communication. As the adaptive meshes being generated reach billions of elements and the analyses are performed on massively parallel computers with 100,000’s of computing cores, a number of complexities arise that need to be addressed. This paper presents procedures developed to deal with two of them. The first is a procedure to support multiple parts per processor which is used as the mesh increases in size and it is desirable to partition the mesh to a larger number of computing cores than are currently being used. The second is a predictive load balancing method that sets entity weights before dynamic load balancing steps so that the mesh is well balanced after the mesh adaptation step thus avoiding excessive memory spikes that would otherwise occur during mesh adaptation.

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Acknowledgments

We gratefully acknowledge the support of this work by NSF’s PetaApps program under grant OCI-0749152 and by the DOE Office of Science’s SciDAC/ITAPS program under grant DE-FC02-06ER25769. This research used computing resources provided by: (a) Rensselaer’s Computational Center for Nanotechnology Innovations that is funded by the State of New York, IBM and Rensselaer Polytechnic Institute, (b) Argonne Leadership Computing Facility at Argonne National Laboratory, which is supported by the Office of Science of the DOE under contracts DE-AC02-06CH11357, and (c) NSF through TeraGrid resources provided by National Institute for Computational Sciences (NICS); Kraken system was used. We would like to acknowledge that results presented in this article made use of software components provided by ACUSIM Software Inc. and Simmetrix Inc.

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Authors and Affiliations

  1. Scientific Computation Research Center, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA

    Min Zhou, Ting Xie, Seegyoung Seol, Mark S. Shephard & Onkar Sahni

  2. Department of Aerospace Engineering Sciences, University of Colorado at Boulder, 429 UCB, Boulder, CO, 80309, USA

    Kenneth E. Jansen

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  1. Min Zhou
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  2. Ting Xie
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  3. Seegyoung Seol
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  4. Mark S. Shephard
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  5. Onkar Sahni
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  6. Kenneth E. Jansen
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Correspondence to Mark S. Shephard.

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Zhou, M., Xie, T., Seol, S. et al. Tools to support mesh adaptation on massively parallel computers. Engineering with Computers 28, 287–301 (2012). https://doi.org/10.1007/s00366-011-0218-x

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