volumevisualization is an important tool in many scientific applications, requiring intensive processing and dealing with large amounts of data. Therefore the size of these data always exceeds the processing and visu...
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ISBN:
(纸本)0769513301
volumevisualization is an important tool in many scientific applications, requiring intensive processing and dealing with large amounts of data. Therefore the size of these data always exceeds the processing and visualization capacities of the conventional workstations. In this technical poster we present our working in progress about techniques of data prefetching and data prediction to volumevisualization on low cost PC clusters. Our approach is based on the Distributed Shared Memory (DSM) paradigm [3], which one can greatly facilitate the parallel programming efforts on volumevisualization applications.
We have developed a highly efficient, high fidelity approach for parallelvolume rendering that is called permutation warping. Permutation warping may use any one pass filter kernel, an example of which is trilinear r...
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ISBN:
(纸本)0819427381
We have developed a highly efficient, high fidelity approach for parallelvolume rendering that is called permutation warping. Permutation warping may use any one pass filter kernel, an example of which is trilinear reconstruction, an advantage over the shear warp approach. This work discusses experiments in improving permutation warping using data dependent optimizations to make it more competitive in speed with the shear warp algorithm. We use a linear octree on each processor for collapsing homogeneous regions and eliminating empty space. Static load balancing is also used to redistribute nodes from a processor's octree to achieve higher efficiencies. In studies on a 16384 processor MasPar MP-2, we have measured improvements of 3 to 5 times over our previous results. Run times are 73 milliseconds, 29 Mvoxels/second, or 14 frames/second for 128(3) volumes, the fastest MasPar volume rendering numbers in the literature. nun times are 427 milliseconds, 39 Mvoxels/second, or 2 frames/second for 256(3) volumes. The performance numbers show that coherency adaptations are effective for permutation warping. Because permutation warping has good scalability characteristics, it proves to be a superior approach for massively parallel computers when image fidelity is a required feature. We have provided further evidence for the utility of permutation warping as a scalable, high fidelity, and high performance approach to parallel volume visualization.
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