X-ray diffraction experiments are used extensively in the sciences to study the structure, chemicalcomposition and physical properties of materials. The output of such experiments are samples of thediffraction pattern, which essentially constitutes a 3D unstructured dataset. In this thesis, wedevelop a method for visualizing such datasets.Our visualization method is based on volume ray casting, but operates directly on the unstructuredsamples, rather than resampling them to form voxels. We estimate the intensity of the X-raydiffraction pattern at points along the rays by interpolation using nearby samples, taking advantageof an octree to facilitate efficient range search. The method is implemented on both the CPUand 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 sand 200 s, depending upon dataset, and settings used. A simple performance model is developedand empirically tested to better understand this variation. Our implementation scales exceptionallywell to more CPU cores, with a speedup of 5.9 on a 6-core CPU. Furthermore, the GPU implementationachieves a speedup of around 4.6 compared to the CPU version.