Real-time data processing for ultrafast X-ray computed tomography using modular CUDA based pipelines

In this article, a new version of the Real-time Image Stream Algorithms (RISA) data processing suite is introduced. It now features online detector data acquisition, high-throughput data dumping and enhanced real-time data processing capabilities. The achieved low-latency real-time data processing extends the application of ultrafast electron beam X-ray computed tomography (UFXCT) scanners to real-time scanner control and process control. We implemented high performance data packet reception based on data plane development kit (DPDK) and high-throughput data storing using both hierarchical data format version 5 (HDF5) as well as the adaptable input/output system version 2 (ADIOS2). Furthermore, we extended RISA's underlying pipelining framework to support the fork-join paradigm. This allows for more complex workflows as it is necessary, e.g. for online data processing. Also, the pipeline configuration is moved from compile-time to runtime, i.e. processing stages and their interconnections can now be configured using a configuration file. In several benchmarks, RISA is profiled regarding data acquisition performance, data storage throughput and overall processing latency. We found that using direct IO mode significantly improves data writing performance on the local data storage. We could further prove that RISA is now capable of concurrently receiving, processing and storing data from up to 768 detector channels (3072 MB/s) at 8000 fps on a single-GPU computer in real-time.