Parallel Volume Rendering with Sparse Data Structures

Direct volume rendering is a popular technique for scientifc visualization. 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 object space coherence, we propose to employ two sparse-matrix representation schemes as spatial data structures. To further reduce the processing time, we employ 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 reduce the processing time as well as the memory storage requirement; and (b) parallel processing allows us to further speed up the volume rendering process. Experiments were conducted to assess our proposed scheme. Results show that our proposed data parallel algorithms performed well on two different parallel distributed memory systems.