In the realm of AI networks, the health of physical links is paramount to ensuring optimal performance and reliability. At Meta, we recognize that robust physical connectivity is crucial for the seamless operation of AI workloads, which demand high-speed and reliable data transmission. This presentation will delve into Meta's comprehensive strategy for maintaining healthy physical links within our AI networks.
We will explore the significance of link health in AI networks, emphasizing how it impacts overall system efficiency and performance. Meta employs advanced physical layer diagnostics, including Pseudo-Random Binary Sequence (PRBS) and Forward Error Correction (FEC) diagnostics, to rigorously test and validate link integrity before deployment into production. These diagnostics help identify potential issues, ensuring only healthy links are operational.
Furthermore, we will discuss Meta's proactive approach to managing link health in production environments. Unhealthy links are swiftly removed from service, and an automated triage pipeline is employed to facilitate effective repairs. This pipeline not only enhances the speed and accuracy of link restoration but also minimizes downtime, thereby maintaining the high reliability standards expected in AI network operations.
