Parallel Shear-Warp Factorization Volume Rendering Using Efficient 1-D and 2-D Partitioning Schemes for Distributed Memory Multicomputers

  • Authors:

  • Ching-Feng Lin;Don-Lin Yang;Yeh-Ching Chung

  • Affiliations:

  • Department of Information Engineering, Feng Chia University, Taichung 407, Taiwan cflin@fcu.edu.tw;Department of Information Engineering, Feng Chia University, Taichung 407, Taiwan dlyang@fcu.edu.tw;Department of Information Engineering, Feng Chia University, Taichung 407, Taiwan ychung@fcu.edu.tw

  • Venue:
  • The Journal of Supercomputing
  • Year:
  • 2002

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Abstract

3-D data visualization is very useful for medical imaging and computational fluid dynamics. Volume rendering can be used to exhibit the shape and volumetric properties of 3-D objects. However, volume rendering requires a considerable amount of time to process the large volume of data. To deliver the necessary rendering rates, parallel hardware architectures such as distributed memory multicomputers offer viable solutions. The challenge is to design efficient parallel algorithms that utilize the hardware parallelism effectively. In this paper, we present two efficient parallel volume rendering algorithms, the 1D-partition and 2D-partition methods, based on the shear-warp factorization for distributed memory multicomputers. The 1D-partition method has a performance bound on the size of the volume data. If the number of processors is less than a threshold, the 1D-partition method can deliver a good rendering rate. If the number of processors is over a threshold, the 2D-partition method can be used. To evaluate the performance of these two algorithms, we implemented the proposed methods along with the slice data partitioning, volume data partitioning, and sheared volume data partitioning methods on an IBM SP2 parallel machine. Six volume data sets were used as the test samples. The experimental results show that the proposed methods outperform other compatible algorithms for all test samples. When the number of processors is over a threshold, the experimental results also demonstrate that the 2D-partition method is better than the 1D-partition method.