An Extensible Parallel Computing Framework for Ultra-Fast X-Ray Imaging

Moore’s law stays the driving force behind higher chip integration density and an everincreasing number of transistors. However, the adoption ofmassively parallel hardware architectures widens the gap between the potentially available microprocessor performance and the performance a developer can make use of. is thesis tries to close this gap by solving the problems that arise from the challenges of achieving optimal performance on parallel compute systems, allowing developers and end-users to use this compute performance in a transparent manner and using the compute performance to enable data-driven processes. A general solution cannot realistically achieve optimal operation which is why we will focus on streamed data processing in this thesis. Data streams lend themselves to describe high-throughput data processing tasks such as audio and video processing. With this specific data stream use case, we can systematically improve the existing designs and optimize the execution from the instruction-level parallelism up to node-level task parallelism. In particular, we want to focus on X-ray imaging applications used at synchrotron light sources. ese large-scale facilities provide an X-ray beam that enables scanning samples at much higher spatial and temporal resolution compared to conventional X-ray sources. e increased data rate inevitably requires highly parallel processing systems as well as an optimized data acquisition and control environment. To solve the problem of high-throughput streamed data processing we developed, modelled and evaluated system architectures to acquire and process data streams on parallel and heterogeneous compute systems. We developed a method to map general task descriptions onto heterogeneous compute systems and execute them with optimizations for local multi- machines and clusters of multi- compute nodes. We also proposed an source-to-source translation system to simplify the development of task descriptions. We have shown that it is possible to acquire and compute tomographic reconstructions on a heterogeneous compute system consisting of  and  in so real-time. e end-user’s only responsibility is to describe the problem correctly. With the proposed system architectures, we paved the way for novel in-situ and in-vivo experiments and a much smarter experiment setup in general. Where existing experiments depend on a static environment and process sequence, we established the possibility to control the experiment setup in a closed feedback loop.