Low GPU utilization on multi-GPU instance: diagnose and right-size
Tests distributed bottleneck triage. Strong answers profile CPU/GPU/disk, compare gradient sync time to compute, validate per-GPU batch size, and check NVLink vs PCIe. Red flag: suggesting more GPUs before ruling out data starvation or all-reduce overhead.
Tests whether you can decompose distributed training inefficiency into hardware, software, and algorithmic bottlenecks. A strong answer builds a diagnostic funnel: first profile data loading and CPU preprocessing; second, measure all-reduce versus compute to catch gradient sync overhead on PCIe versus NVLink; third, verify per-GPU batch size saturates CUDA cores; fourth, check for DistributedDataParallel instead of DataParallel. Red flag: proposing more GPUs without quantifying the bottleneck, which worsens communication overhead.
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- #mlops
- #distributed-training
- #gpu-utilization
- #performance-debugging
- #deep-learning
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