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Visualizing Network Traffic to Understand the Performance of Massively Parallel Simulations.

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  • 1Scientific Computing and Imaging Institute, University of Utah, USA. aaditya@sci.utah.edu

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Summary
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This study visualizes supercomputer network traffic to help developers understand and optimize application performance. The developed tool aids in identifying network bottlenecks for improved massively parallel application efficiency.

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Area of Science:

  • High-performance computing
  • Computer science
  • Network visualization

Background:

  • Massively parallel application performance is often limited by communication costs.
  • Supercomputer network management is complex due to increasing scale and intricacy.
  • Understanding network activity is crucial for optimizing parallel applications.

Purpose of the Study:

  • To introduce a visualization tool for exploring packet flow in supercomputer interconnects.
  • To aid parallel application developers in diagnosing network-related performance issues.
  • To enhance the understanding of network behavior in large-scale computing environments.

Main Methods:

  • Development of a visualization tool employing two linked views: a 2D network structure projection and a 3D physical topology view.
  • Utilizing visualization to enable detailed exploration of packet flow through hardware interconnects.
  • Case study application using the pF3D multi-physics code on an IBM Blue Gene/P system.

Main Results:

  • The visualization tool provides valuable insights into network activity.
  • The tool facilitates the identification of trends and patterns in network traffic.
  • The case study demonstrated the tool's effectiveness in explaining and optimizing application performance.

Conclusions:

  • Visualization techniques are effective for understanding and optimizing supercomputer network performance.
  • The developed tool offers a practical solution for developers to analyze complex network data.
  • Improved network understanding leads to enhanced performance for massively parallel applications.