Rapid data processing for ultrafast X-ray computed tomography using scalable and modular CUDA based pipelines

Ultrafast X-ray tomography is an advanced imaging technique for the study of dynamic processes basing on the principles of electron beam scanning. A typical application case for this technique is e.g. the study of multiphase flows, that is, flows of mixtures of substances such as gas-liquidflows in pipelines or chemical reactors. At Helmholtz-Zentrum Dresden-Rossendorf (HZDR) a number of such tomography scanners are operated. Currently, there are two main points limiting their application in some fields. First, after each CT scan sequence the data of the radiation detector must be downloaded from the scanner to a data processing machine. Second, the current data processing is comparably time-consuming compared to the CT scan sequence interval. To enable online observations or use this technique to control actuators in real-time, a modular and scalable data processing tool has been developed, consisting of user-definable stages working independently together in a so called data processing pipeline, that keeps up with the CT scanner's maximal frame rate of up to 8  kHz. The newly developed data processing stages are freely programmable and combinable. In order to achieve the highest processing performance all relevant data processing steps, which are required for a standard slice image reconstruction, were individually implemented in separate stages using Graphics Processing Units (GPUs) and NVIDIA's CUDA programming language. Data processing performance tests on different high-end GPUs (Tesla K20c, GeForce GTX 1080, Tesla P100) showed excellent performance.Program summary/new version program summaryProgram Title: GLADOS/RISAProgram Files doi: http://dx.doi.org/10.17632/65sx747rvm.1 Licensing provisions: LGPLv3Programming language: C++/CUDASupplementary material: Test data set, used for the performance analysis.Nature of problem: Ultrafast computed tomography is performed with a scan rate of up to 8 kHz. To obtain cross-sectional images from projection data computer-based image reconstruction algorithms must be applied. The objective of the presented program is to reconstruct a data stream of around 1.3  GB  s−1 in a minimum time period. Thus, the program allows to go into new fields of application and to use in the future even more compute-intensive algorithms, especially for data post-processing, to improve the quality of data analysis.Solution method: The program solves the given problem using a two-step process: first, by a generic, expandable and widely applicable template library implementing the streaming paradigm (GLADOS); second, by optimized processing stages for ultrafast computed tomography implementing the required algorithms in a performance-oriented way using CUDA (RISA). Thereby, task-parallelism between the processing stages as well as data parallelism within one processing stage is realized.