J. Liu, C.-H. Huang, and D.-L. Yang (Taiwan)
parallel algorithm, volume rendering, sparse data structure, cluster computing
Direct volume rendering is a popular technique for scientific visualization recently. The computation cost of direct volume rendering increases exponentially as the size of the volume dataset increases. Hence, efficient volume rendering has become an important issue. In this work we study parallel volume rendering algorithms based on sparse data structures. In order to exploit the object space coherence, we propose to employ two sparse-matrix representation schemes as spatial data structures. To further reduce the processing time, we study data-parallel volume rendering algorithms based on sparse data structures. Two distinct features of our work are: (a) the sparse data structures enable us to save memory storage requirement as well as processing time; and (b) the parallel processing allows us to further speed up the volume rendering process. Experiments are conducted to assess our proposed scheme. Results show that our proposed data parallel algorithms perform and scale very well on two different parallel distributed memory systems.
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