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Synthesis and Microdiffraction at Extreme Pressures and Temperatures
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Published on: October 7, 2013

Real science at the petascale.

Radhika S Saksena1, Bruce Boghosian, Luis Fazendeiro

  • 1Centre for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, UK.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|May 20, 2009
PubMed
Summary

Computational science research leveraged petascale computing for breakthroughs in turbulence, materials science, and biomedical fields. This work demonstrates the effectiveness of petascale resources and traditional parallel programming for scientific discovery.

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Area of Science:

  • Computational Science
  • High-Performance Computing
  • Scientific Research

Background:

  • Petascale computing resources offer unprecedented capabilities for scientific investigation.
  • The TeraGrid 'petascale' resource, Ranger, was the largest open scientific computing system in the world in early 2008.
  • Advancements in computing power enable research across diverse scientific domains.

Purpose of the Study:

  • To describe computational science research utilizing petascale resources.
  • To demonstrate the achievement of scientific results at unprecedented scales and resolution.
  • To evaluate the performance of parallel programming at the petascale.

Main Methods:

  • Utilizing the petascale supercomputer Ranger for capability computing and high-throughput simulations.
  • Testing parallel performance on up to 32,768 cores for capability computing.
  • Conducting ensemble simulations on 32-512 cores.
  • Reporting parallel performance on the IBM Blue Gene/P system with up to 65,636 cores.

Main Results:

  • Scientific results were achieved at unprecedented scales and resolution across multiple domains.
  • Excellent parallel scaling performance was observed on up to 32,768 cores for specific codes.
  • Conventional parallel programming with MPI proved successful at the petascale.
  • High-throughput simulations were effectively run on intermediate-scale job ranges.

Conclusions:

  • Petascale computing resources are vital for advancing scientific research across various disciplines.
  • Conventional parallel programming methods are viable for petascale environments.
  • The performance of codes on large-scale systems like Ranger and Blue Gene/P validates their utility for scientific discovery.