An Irregular Approach to Large-Scale Computed Tomography on Multiple Graphics Processors Improves Voxel Processing Throughput

While much work has been done on applying GPU technology to computed tomography (CT) reconstruction algorithms, many of these implementations focus on smaller datasets that are better suited for medical applications. This paper proposes an irregular approach to the algorithm design which utilizes the GPU hardware's unique cache structure and employs small x-ray image data prefetches on the host to upload to the GPUs while the devices are operating on large contiguous sub-volumes of the reconstruction. This approach will improve the overall cache hit-rates and thus improve the performance of the massively multithreaded environment of the GPU. Overall, utilizing small prefetches of x-ray image data improved the volumetric pixel (voxel) processing rate when compared to utilizing large data prefetches which would minimize data transfers and kernel launches. Additionally, this approach does not sacrifice performance on small datasets and is thus suitable for medical and industrial applications. This work utilizes the CUDA programming environment and Nvidia's Tesla GPUs.