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Kwan-Liu Ma, UC Davis

Parallel Distance Field Computing

Depiction of a distance field computed from a feature surface in data generated by a combustion simulation.


  • Distance field computing is fundamental to many data analysis and visualization applications.
  • This project realizes a highly scalable parallel implementation to support in situ processing and data reduction.
  • The design is general to handle a variety of data
  • The  product is a standalone library to be easily adopted for different settings.

Applications & Impact

  • Distance fields can be used as importance fields to guide rendering, data compression, sampling, and feature-based optimizations.
  • The resulting technology will benefit many SciDAC applications from combustion, fusion, to climate and astrophysics simulations.
  • This work will motivate others to develop novel visualization and data reduction methods using distance fields.


  • A use case on a turbulent combustion simulation shows over 80% data reduction while revealing previously hidden flow features.
  • A parallel spatial data structure has been designed to accelerate the computation.
  • Tests on the parallel implementation show the data that must be exchanged is under 0.01% of the total data, and the cost to exchange the data is under 0.2% of the overall time.