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Philippe Lacroute, "Analysis of a Parallel Volume Rendering System Based on the ShearWarp Factorization," IEEE Transactions on Visualization and Computer Graphics, vol. 2, no. 3, pp. 218231, September, 1996.  
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@article{ 10.1109/2945.537305, author = {Philippe Lacroute}, title = {Analysis of a Parallel Volume Rendering System Based on the ShearWarp Factorization}, journal ={IEEE Transactions on Visualization and Computer Graphics}, volume = {2}, number = {3}, issn = {10772626}, year = {1996}, pages = {218231}, doi = {http://doi.ieeecomputersociety.org/10.1109/2945.537305}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Visualization and Computer Graphics TI  Analysis of a Parallel Volume Rendering System Based on the ShearWarp Factorization IS  3 SN  10772626 SP218 EP231 EPD  218231 A1  Philippe Lacroute, PY  1996 KW  Volume rendering KW  parallel algorithms for shared memory multiprocessors KW  shearwarp factorization KW  coherence optimizations KW  image partition KW  multiprocessor performance analysis. VL  2 JA  IEEE Transactions on Visualization and Computer Graphics ER   
Abstract—This paper presents a parallel volume rendering algorithm that can render a 256 × 256 × 225 voxel medical data set at over 15 Hz and a 512 × 512 × 334 voxel data set at over 7 Hz on a 32processor Silicon Graphics Challenge. The algorithm achieves these results by minimizing each of the three components of execution time: computation time, synchronization time, and data communication time. Computation time is low because the parallel algorithm is based on the recentlyreported shearwarp serial volume rendering algorithm which is over five times faster than previous serial algorithms. The algorithm uses runlength encoding to exploit coherence and an efficient volume traversal to reduce overhead. Synchronization time is minimized by using dynamic load balancing and a task partition that minimizes synchronization events. Data communication costs are low because the algorithm is implemented for sharedmemory multiprocessors, a class of machines with hardware support for lowlatency finegrain communication and hardware caching to hide latency.
We draw two conclusions from our implementation. First, we find that on sharedmemory architectures data redistribution and communication costs do not dominate rendering time. Second, we find that cache locality requirements impose a limit on parallelism in volume rendering algorithms. Specifically, our results indicate that sharedmemory machines with hundreds of processors would be useful only for rendering very large data sets.
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