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High-Performance Host-Device Scheduling and Data-Transfer Minimization Techniques for Visualization of 3D Agent-Based

N Seekhao1, G Yu2, S Yuen2

  • 1Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA.

PDPTA '19 : Proceedings of the 2019 International Conference on Parallel & Distributed Processing Techniquess & Applications. International Conference on Parallel and Distributed Processing Techniques and Applications (2019 : Las Vegas
|October 30, 2020
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This summary is machine-generated.

This study introduces GPU hyper-tasking for real-time in-situ visualization of massive simulation data. The technique minimizes data transfers, enabling interactive exploration of 1.7 billion protein data points at 42.8 fps.

Area of Science:

  • Computational science
  • Scientific visualization
  • High-performance computing

Background:

  • High-fidelity numerical simulations generate vast datasets, necessitating efficient analysis methods.
  • Real-time in-situ visualization is crucial for understanding complex phenomena but faces challenges with large data volumes and CPU-GPU communication overhead.

Purpose of the Study:

  • To develop a novel scheduling scheme for real-time in-situ visualization of massive simulation data.
  • To enhance interactivity and reduce computational costs in large-scale data analysis.

Main Methods:

  • Implementation of GPU hyper-tasking to enable simultaneous processing and visualization.
  • Development of an activity-aware technique to minimize redundant CPU-GPU data copies.
  • Creation of a simulation platform capable of handling billions of data points.
Keywords:
heterogeneous platform schedulinghigh-performance agent-based modelingreal-time in situ visualizationvocal fold modelwound healing

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Main Results:

  • Achieved real-time simulation and interactivity through GPU hyper-tasking.
  • Reduced CPU-GPU communication by minimizing redundant data transfers.
  • Demonstrated visualization of 1.7 billion protein data points in situ with an average frame rate of 42.8 fps.

Conclusions:

  • The proposed scheduling scheme effectively enables real-time in-situ visualization for massive datasets.
  • This approach allows for interactive exploration of large-scale simulation data on remote servers during computation.
  • GPU hyper-tasking and optimized data transfer are key to overcoming current visualization bottlenecks.