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Explore Reflective Memory vs. Ethernet solutions for controlling beam parameters in the LCLS Fast Feedback project. Understand the physics requirements, time budget, and hardware solutions needed. Future work includes testing Ethernet with multicast for performance.
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Reflective Memory vs. EthernetEvaluating Data Network Hardware Solutions for LCLS Fast Feedback ControlsMarya Pearson
Introduction • The LCLS Fast Feedback project aims to provide a method for controlling the beam parameters and deliver a stable XFEL. • Feedback a technique for controlling these parameters • collect measurements • evaluate algorithms • produce instructions for action • Physics Requirement Document (PRD) outlines all specifications for a data network that will facilitate feedback.
The PRD • Describes the parameters we wish to control. • Transverse • Longitudinal • Launch • Here, the time budget is introduced. • Data Network requirements • Deterministic • Scalable • Low-Latency • Affordable
1+1+6 = 8ms 1ms BPMs send data 1ms for fast feedback ~6ms for magnets to react In some cases time budget is adjustable more or less. According to the Physics Requirement Document (PRD) the machine operates at 120 Hz and thereby the 8.3 ms time budget is derived. Time Budget
Time Budget • Based on the 120 Hz constraint • The idea is that at least 7ms of the 8.3 ms are fixed. That leaves the feedback 1ms to regulate.
IOC:IN20:BP02 BPM9-15 IOC:IN20:MG01 XC04, XC07, YC04, YC07 Loop Rate: 10 Hz This means that the Inj.L feedback can afford 100ms, however to support 120 Hz beam operation the system should operate within 8.3 ms. Injector Launch
Data Network Hardware Solutions • Reflective Memory (RM) • Star topology • Ring topology • Ethernet • Point-to-Point • Multicast
Decision and Future Work • Ethernet with Multicast • chosen for first prototype • simulations must be done to measure performance. • Use Cisco products and collaborate with Cisco engineers to set up a test stand.
Acknowledgements • This research was conducted at the U.S. Department of Energy Stanford Linear Accelerator Center. I would like to express my gratitude to my mentors, Ernest Williams Jr. and Sheng Peng. I am also thankful for the assistance of Farah Rahbar, Susan Schultz, and Stephen Rock.