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.本文介绍了实时图像流算法（Real-time Image Stream Algorithms, RISA）数据集处理套件的新版本。该套件现已支持在线探测器数据采集、高通量数据转储与增强型实时数据处理能力。其实现的低延迟实时数据处理功能，将超快电子束X射线计算机断层扫描（ultrafast electron beam X-ray computed tomography, UFXCT）扫描仪的应用场景拓展至实时扫描仪控制与过程控制领域。我们基于数据平面开发套件（data plane development kit, DPDK）实现了高性能数据包接收，并采用分层数据格式第5版（hierarchical data format version 5, HDF5）与自适应输入输出系统第2版（adaptable input/output system version 2, ADIOS2）完成高通量数据存储。此外，我们对RISA的底层流水线框架进行扩展，以支持分叉-合并范式，从而可构建在线数据处理所需的复杂工作流。同时，我们将流水线配置从编译时转移至运行时，即当前可通过配置文件对处理阶段及其互连关系进行灵活配置。在多组基准测试中，我们对RISA的数据采集性能、数据存储吞吐量与整体处理延迟开展了性能剖析。结果显示，启用直接I/O模式可显著提升本地数据存储的写入性能。我们进一步验证表明，新版本RISA现已可在单GPU主机上，以8000帧每秒的速率，实时并发接收、处理并存储来自最多768个探测器通道的数据，峰值速率可达3072 MB/s。