3D Visualization of X-ray Diffraction Data
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.
Place, publisher, year, edition, pages
Institutt for datateknikk og informasjonsvitenskap , 2012. , 136 p.
ntnudaim:7334, MTDT datateknikk, Komplekse datasystemer
IdentifiersURN: urn:nbn:no:ntnu:diva-18903Local ID: ntnudaim:7334OAI: oai:DiVA.org:ntnu-18903DiVA: diva2:566360
Elster, Anne Cathrine, FørsteamanuensisBreiby, Dag W.