Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU
Proceedings of the 37th annual international symposium on Computer architecture
Parallel volume rendering implementation on graphics cards using CUDA
Facing the multicore-challenge
Parallel volume rendering implementation on graphics cards using CUDA
Facing the multicore-challenge
Moguls: a model to explore the memory hierarchy for bandwidth improvements
Proceedings of the 38th annual international symposium on Computer architecture
Heterogeneous computing for vertebra detection and segmentation in x-ray images
Journal of Biomedical Imaging - Special issue on Parallel Computation in Medical Imaging Applications
Proceedings of the 2012 International Workshop on Programming Models and Applications for Multicores and Manycores
Can traditional programming bridge the Ninja performance gap for parallel computing applications?
Proceedings of the 39th Annual International Symposium on Computer Architecture
A GPU-supported lossless compression scheme for rendering time-varying volume data
VG'10 Proceedings of the 8th IEEE/EG international conference on Volume Graphics
Prostate cancer visualization from MR imagery and MR spectroscopy
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
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Medical volumetric imaging requires high fidelity, high performance rendering algorithms. We motivate and analyze new volumetric rendering algorithms that are suited to modern parallel processing architectures. First, we describe the three major categories of volume rendering algorithms and confirm through an imaging scientist-guided evaluation that ray-casting is the most acceptable. We describe a thread- and data-parallel implementation of ray-casting that makes it amenable to key architectural trends of three modern commodity parallel architectures: multi-core, GPU, and an upcoming many-core Intel®architecture code-named Larrabee. We achieve more than an order of magnitude performance improvement on a number of large 3D medical datasets. We further describe a data compression scheme that significantly reduces data-transfer overhead. This allows our approach to scale well to large numbers of Larrabee cores.