We present an end-to-end automated workflow that uses large-scale remote compute resources and an embedded GPU platform at the edge to enable AI/ML-accelerated real-time analysis of data collected for x-ray ptychography. Ptychography is a lensless method that is being used to image samples through a simultaneous numerical inversion of a large number of diffraction patterns from adjacent overlapping scan positions. This acquisition method can enable nanoscale imaging with x-rays and electrons, but this often requires very large experimental datasets and commensurately high turnaround times, which can limit experimental capabilities such as real-time experimental steering and low-latency monitoring. In this work, we introduce a software system that can automate ptychography data analysis tasks. We accelerate the data analysis pipeline by using a modified version of PtychoNN -- an ML-based approach to solve phase retrieval problem that shows two orders of magnitude speedup compared to traditional iterative methods. Further, our system coordinates and overlaps different data analysis tasks to minimize synchronization overhead between different stages of the workflow. We evaluate our workflow system with real-world experimental workloads from the 26ID beamline at Advanced Photon Source and ThetaGPU cluster at Argonne Leadership Computing Resources.

翻译：我们提出了一种端到端的自动化工作流，该工作流利用大规模远程计算资源和边缘端嵌入的GPU平台，实现了AI/ML加速的X射线叠层衍射数据实时分析。叠层衍射是一种无透镜成像方法，通过同时对大量来自相邻重叠扫描位置的衍射图案进行数值反演来对样本成像。这种采集方法可实现X射线和电子的纳米级成像，但通常需要非常庞大的实验数据集以及相应的高周转时间，这限制了实时实验引导和低延迟监测等实验能力。在本工作中，我们引入了一个可自动化叠层衍射数据分析任务的软件系统。我们采用改进版PtychoNN（一种基于机器学习的方法求解相位恢复问题，相比传统迭代方法可实现两个数量级的加速）来加速数据分析管道。此外，我们的系统协调并重叠不同的数据分析任务，以最小化工作流各阶段之间的同步开销。我们利用来自先进光子源26ID光束线和阿贡领导计算资源ThetaGPU集群的真实实验负载评估了该工作流系统。