← 🩺 Fleet Reliability & Observability
Advanced
Stragglers and Hangs
Synchronous training runs at the speed of its slowest rank, so one GPU that is 30% slow makes a thousand GPUs 30% slow, and one rank that never arrives at a collective makes the other 1,023 wait in silence until a watchdog fires ten minutes later. Finding the slow rank and the stuck rank is the most common on-call task on a training fleet, and the tooling for it (per-rank timing, the NCCL flight recorder, stack dumps across ranks) is specific and learnable. This page derives the straggler tax from first principles, lists the causes in the order they actually occur, and gives the procedure for a hang.
Unlock the full curriculum — ₹2,000 / $25every concept + every answer · 6 months · no auto-renew
RELATED CONCEPTS
LESSONS THAT TEACH THIS
PRACTICE THIS IN REAL QUESTIONS
Distributed Training & ParallelismYou see 'Watchdog caught collective operation timeout' in the logs. What happened, and how do you find which rank caused it?→Distributed Training & ParallelismOne GPU out of 16,000 is 15% slow and the whole run is 15% slow. How do you find it, and what is usually wrong with it?→GPU Fleet Reliability & ObservabilityA training run hangs every few hours with no error, and the GPUs sit idle until the timeout fires. Find the cause.→Coding for InfraA multi-GPU training job hangs at step 400 with every GPU at 100 percent utilization. Debug it.→GPU Fleet Reliability & ObservabilityWalk me through reading a collective flight-recorder dump. What is in it, and how do you find the rank that caused a hang?→Kubernetes, Slurm & GPU SchedulingWhat is gang scheduling, and what goes wrong on a Kubernetes cluster that does not have it?→
