A scalable, hybrid scheme for volume rendering massive data sets

  • Authors:
  • Hank Childs;Mark Duchaineau;Kwan-Liu Ma

  • Affiliations:
  • Lawrence Livermore National Laboratory and University of California at Davis;Lawrence Livermore National Laboratory;University of California at Davis

  • Venue:
  • EG PGV'06 Proceedings of the 6th Eurographics conference on Parallel Graphics and Visualization
  • Year:
  • 2006

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Abstract

We introduce a parallel, distributed memory algorithm for volume rendering massive data sets. The algorithm's scalability has been demonstrated up to 400 processors, rendering one hundred million unstructured elements in under one second. The heart of the algorithm is a hybrid approach that parallelizes over both the elements of the input data and over the pixels of the output image. At each stage of the algorithm, there are strong limits on how much work each processor performs, ensuring good parallel efficiency. The algorithm is sample-based. We present two techniques for calculating the sample points: a 3D rasterization technique and a kernel-based technique, which trade off between speed and generality. Finally, the algorithm is very flexible. It can be deployed in general purpose visualization tools and can also support diverse mesh types, ranging from structured grids to curvilinear and unstructured meshes to point clouds.