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• W610576676 abstract "X-ray diffraction experiments are used extensively in the sciences to study the structure, chemical composition and physical properties of materials. The output of such experiments are samples of the diffraction pattern, which essentially constitutes a 3D unstructured dataset. In this thesis, we develop a method for visualizing such datasets. Our visualization method is based on volume ray casting, but operates directly on the unstructured samples, rather than resampling them to form voxels. We estimate the intensity of the X-ray diffraction pattern at points along the rays by interpolation using nearby samples, taking advantage of an octree to facilitate efficient range search. The method is implemented on both the CPU and the GPU. To test our method, actual X-ray diffraction datasets is used, consisting of up to 120M samples. We are able to generate images of good quality. The rendering time varies dramatically, between 5 s and 200 s, depending upon dataset, and settings used. A simple performance model is developed and empirically tested to better understand this variation. Our implementation scales exceptionally well to more CPU cores, with a speedup of 5.9 on a 6-core CPU. Furthermore, the GPU implementation achieves a speedup of around 4.6 compared to the CPU version." @default.
• W610576676 created "2016-06-24" @default.
• W610576676 creator A5033903615 @default.
• W610576676 date "2012-01-01" @default.
• W610576676 modified "2023-09-24" @default.
• W610576676 title "3D Visualization of X-ray Diffraction Data" @default.
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• W610576676 hasPublicationYear "2012" @default.
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