Operating large-scale GPU training clusters (such as thousands of NVIDIA H100s or B200s interconnected via 3.2 Tbps InfiniBand) introduces an entirely new observability and operational domain. A single GPU rank stalling due to a thermal throttle or a dropped packet in a collective AllReduce ring halts all thousands of GPUs synchronously.
Without precision observability into arithmetic intensity, collective communication patterns, and hardware telemetry, engineering teams risk burning millions of dollars on sub-optimal cluster efficiency.
This module deconstructs:
- Efficiency Metrics: Deriving theoretical peak FLOPs, Model FLOPs Utilization (MFU), and Hardware FLOPs Utilization (HFU).
- Communication Diagnostics: Profiling NVIDIA Collective Communications Library (NCCL) ring/tree algorithms, detecting straggler ranks, and tracing InfiniBand PFC/ECN congestion.
- Hardware Telemetry & Automated Remediation: NVIDIA Data Center GPU Manager (DCGM), XID error classification, uncorrectable PCIe/ECC errors, and automated cordon/drain pipelines.