[论文解读] Compute System Organization for High Frequency High Order Wavefront Sensing and Control

Barry Lyu, Vaibhavi Manjarekar+1 more|arXiv (Cornell University)|Feb 23, 2026

Stellar, planetary, and galactic studies被引用 0

一句话总结

论文提出将 Habitable Worlds Observatory 的高频高阶波前传感与控制（HOWFSC）工作负载移交给在 Sun–Earth L2 位位的专用同轨计算卫星，并分析性能、内存与体系结构选项，以实现十到百赫兹的控制速率。

ABSTRACT

Maintaining long-term wavefront stability is critical for the Habitable Worlds Observatory (HWO), which targets contrasts approaching $10^{-10}$ and therefore requires continuous dark-zone maintenance using high-order wavefront sensing and control (HOWFSC). Prior work has advanced HOWFSC algorithms and profiled candidate implementations on radiation-hardened processors, highlighting a substantial gap between the computational demands of LUVOIR-scale HOWFSC and the capabilities of current onboard spacecraft hardware. In this paper, we argue that this gap can be closed by offloading the HOWFSC pipeline to a dedicated co-flying compute satellite at Sun-Earth L2. This approach enables the use of modern, radiation-tolerant high-performance processors without increasing risk to the primary observatory. We show that such an architecture can increase the end-to-end control cadence from the sub-hertz regime typical of radiation-hardened onboard processing or ground-in-the-loop operation to tens and even hundreds of hertz. We evaluate commercial hardware platforms in terms of performance and feasibility, and we propose custom architectures that enable higher control frequencies with significant power consumption reductions. Finally, we outline system-level considerations for co-flying compute, including reliability, satellite integration, and inter-satellite communication constraints.

研究动机与目标

• 为 Habitable Worlds Observatory（目标保持极端对比度至 10^-10）的高频 HOWFSC 的必要性提供动机。
• 识别 LUVOIR 规模的 HOWFSC 需求与 RadHard 机载硬件之间的计算缺口。
• 提出共飞计算作为解决方案，在确保观测站可靠性的同时实现更高的节拍。
• 评估 HOWFSC 离载的硬件性能、内存带宽与体系结构选项。

提出的方法

• 主张在望远镜附近的专用共飞计算卫星可实现现代化、耐辐射的高性能硬件。
• 使用屋顶线模型（roofline model）表征不同硬件上 HOWFSC 内核的计算/内存瓶颈。
• 在不同带宽/延迟情景下分析卸载延迟和星间链路约束。
• 通过 GEMV 划分实现 EFC/EKF 内核的并行化，以随处理元素扩展。
• 评估基于 GPU 的和 RadHard/离线计算选项，包括多 GPU 设置的时序分解与延迟改进。

Figure 1 : Impact of wavefront sensing cadence on contrast by Pueyo et al [ 14 ] , annotated with achievable cadence and contrast of GITL and on-board BAE5545 for the LUVOIR A system with $\Delta\text{wf}=$ $1.70\text{\,}\mathrm{pm}\text{/}\mathrm{s}$ .

实验结果

研究问题

• RQ1 当前硬件对 LUVOIR 规模暗井场景下高频 HOWFSC 的瓶颈是什么？
• RQ2 共飞计算卫星能否缩小计算缺口，使 HWO 的控制节拍达到十到百赫兹？
• RQ3 实现目标控制频率需要哪些内存、带宽与通信要求？
• RQ4 哪些体系结构选择（内存技术、并行化策略）最适合支持 HOWFSC 的卸载？

主要发现

• 将 HOWFSC 卸载到靠近望远镜的共飞卫星可以显著提升控制节拍，从亚赫兹提升到十几至百赫兹。
• 对于高频 HOWFSC 来说，内存带宽和内存容量，而非原始计算吞吐量，是主要瓶颈。
• 屋顶线分析显示，HOWFSC 内核在 RadHard 与高端商用平台上都是内存绑定，EFC 与预计算矩阵运算占主导。
• 基于 GEMV 的在处理单元间的并行分布可以降低每个处理器的带宽与计算需求，从而实现可扩展实现。
• 基于 GPU 的设计在单个 Nvidia B200 上可实现约 ~35.5 Hz，三块 GPU 时可达约 ~103.7 Hz，但 GPU 在太空应用中引入能耗与确定性挑战。
• 提供高带宽与高容量的内存技术（如 HBM、ReRAM、MRAM）对于满足需求至关重要，而精度与内存占用是需要考虑的重要因素。

Figure 2 : a) Upper-bound of control frequency relative to link latency and bandwidth, assuming infinite compute, symmetric spatial links, and dithering-based estimation (pairwise probing will have higher demand on link bandwidth, scaling with the number of probes). Frequencies are capped to $200\te