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Related Concept Videos

Parallel Processing01:20

Parallel Processing

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Evaluating the performance of parallel subsurface simulators: An illustrative example with PFLOTRAN.

G E Hammond1, P C Lichtner2, R T Mills3

  • 1Applied Systems Analysis and Research, Sandia National Laboratories Albuquerque, New Mexico, USA.

Water Resources Research
|December 16, 2014
PubMed
Summary

This study evaluates the performance of the PFLOTRAN code for subsurface simulations on the Jaguar supercomputer. PFLOTRAN demonstrates strong scalability for various realistic scenarios, aiding subsurface scientists in predicting simulator performance.

Keywords:
biogeochemical transportgroundwater flowhigh performance computing

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Area of Science:

  • Geosciences
  • Computational Science
  • Environmental Engineering

Background:

  • Parallel computing is crucial for complex subsurface simulations.
  • Accurate performance data for simulation codes is needed by scientists.
  • PFLOTRAN is a widely used code for reactive multiphase flow and biogeochemical transport.

Purpose of the Study:

  • To assess the parallel performance of the PFLOTRAN code.
  • To provide subsurface scientists with expected performance metrics for realistic modeling scenarios.
  • To evaluate scalability on the Jaguar supercomputer.

Main Methods:

  • Investigated PFLOTRAN performance on the Jaguar supercomputer.
  • Conducted strong and weak scalability analyses.
  • Evaluated performance across three realistic modeling scenarios: in situ leaching, regional doublet flow, and uranium complexation.

Main Results:

  • PFLOTRAN exhibits strong scalability for the tested realistic scenarios.
  • Performance was analyzed in relation to model design, software, algorithms, and hardware.
  • Detailed discussion of weak scalability for a regional doublet problem is included.

Conclusions:

  • PFLOTRAN shows promising parallel performance for subsurface modeling.
  • The findings inform scientists about the code's capabilities on high-performance computing systems.
  • Understanding scalability is key for effective application of PFLOTRAN in complex environmental simulations.