A solar Ground Layer Adaptive Optics (GLAO) system was considered for the 2.5-meter wide-filed and high-resolution solar telescope (WeHoST) under construction in China. Five separated correlation Shark-Hartmann wavefront sensors (SH-WFS), including one small FoV, high-order SH-WFS and four large FoV, low-order SH-WFS, and a 931-element deformable mirror are employed to enhance the image quality within 5 arcminutes field of view (FoV). Compared to the center of gravity algorithm, the correlation algorithm demands higher computational resources. To solve this problem, a clustered architecture platform based on multi-core CPUs was employed for the real-time controller (RTC) of the 2.5-meter solar GLAO system. The clustered architecture consists of five slope calculation nodes, each corresponding to one of the five SH-WFS, as well as a control node. Subimages from five SH-WFSs are acquired synchronously and the slopes are calculated in the slope calculation nodes.

The control node collects slopes from the slope calculation nodes and performs the wavefront reconstruction. The clustered architecture platform has the benefit of the computing power and scalability. Moreover, choosing multi-core CPUs reduces hardware design complexity and offers enhanced flexibility. The clustered architecture is currently undergoing testing. This report primarily focuses on the performance analysis of individual node computation latency and inter-node communication latency.

Nanfei Yan, Institute of Optics and Electronics