A divide and conquer strategy for scaling weather simulations with multiple regions of interest

  • Authors:
  • Preeti Malakar;Thomas George;Sameer Kumar;Rashmi Mittal;Vijay Natarajan;Yogish Sabharwal;Vaibhav Saxena;Sathish S. Vadhiyar

  • Affiliations:
  • Department of Computer Science and Automation, Indian Institute of Science, Bangalore, India;IBM India Research Lab, New Delhi, India;IBM T.J. Watson Research Center, Yorktown Heights, NY, USA;IBM India Research Lab, New Delhi, India;Department of Computer Science and Automation, Indian Institute of Science, Bangalore, India and Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore, India;IBM India Research Lab, New Delhi, India;IBM India Research Lab, New Delhi, India;Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore, India

  • Venue:
  • Scientific Programming - Selected Papers from Super Computing 2012
  • Year:
  • 2013

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Abstract

Accurate and timely prediction of weather phenomena, such as hurricanes and flash floods, require high-fidelity compute intensive simulations of multiple finer regions of interest within a coarse simulation domain. Current weather applications execute these nested simulations sequentially using all the available processors, which is sub-optimal due to their sub-linear scalability. In this work, we present a strategy for parallel execution of multiple nested domain simulations based on partitioning the 2-D processor grid into disjoint rectangular regions associated with each domain. We propose a novel combination of performance prediction, processor allocation methods and topology-aware mapping of the regions on torus interconnects. Experiments on IBM Blue Gene systems using WRF show that the proposed strategies result in performance improvement of up to 33% with topology-oblivious mapping and up to additional 7% with topology-aware mapping over the default sequential strategy.