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      Improving Scalability of BRAMS: a Regional Weather Forecast Model
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      Chapter

      Improving Scalability of BRAMS: a Regional Weather Forecast Model

      DOI link for Improving Scalability of BRAMS: a Regional Weather Forecast Model

      Improving Scalability of BRAMS: a Regional Weather Forecast Model book

      Improving Scalability of BRAMS: a Regional Weather Forecast Model

      DOI link for Improving Scalability of BRAMS: a Regional Weather Forecast Model

      Improving Scalability of BRAMS: a Regional Weather Forecast Model book

      Edited ByLaxmikant V. Kale, Abhinav Bhatele
      BookParallel Science and Engineering Applications

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      Edition 1st Edition
      First Published 2013
      Imprint CRC Press
      Pages 37
      eBook ISBN 9780429194146
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      ABSTRACT

      National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign

      Jairo Panetta

      Instituto Tecnologico de Aeronautica – ITA

      8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 8.2 Load Balancing Strategies for Weather Models . . . . . . . . . . . . . . . . . 151 8.3 The BRAMS Weather Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 8.4 Load Balancing Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

      8.4.1 Adaptations to AMPI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 8.4.2 Balancing Algorithms Employed . . . . . . . . . . . . . . . . . . . . . . . . 157

      8.5 New Load Balancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 8.6 Fully Distributed Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162

      8.6.1 Hilbert Curve-Based Load Balancer . . . . . . . . . . . . . . . . . . . . . 162 8.6.2 Diffusion-Based Load Balancer . . . . . . . . . . . . . . . . . . . . . . . . . . 164

      8.7 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 8.7.1 First Set of Experiments: Privatization Strategy . . . . . . . 166 8.7.2 Second Set of Experiments: Virtualization Effects . . . . . . 167 8.7.3 Third Set of Experiments: Centralized Load Balancers . 171 8.7.4 Fourth Set of Experiments: Distributed Load Balancers 174

      8.7.4.1 Centralized versus Distributed Load Balancers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175

      8.7.4.2 DiffusionLB versus HilbertLB . . . . . . . . . . . . . 178 8.8 Final Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182

      Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184

      Parallel Approach

      Numerical weather forecasting models have a long history. Currently they have become important decision tools in many different areas, ranging from agriculture to flight control and energy production. Many applications require forecasts with very high resolution. To execute these models in a feasible amount of time, they are typically run in parallel.

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