AI Storage Latency Tracing for GPU Workload Bottlenecks

AI storage latency tracing follows the data path from storage endpoint through the network to the GPU, measuring latency at each hop to localize where the slowdown is — because slow storage looks like idle GPUs, and fixing the wrong hop does not fix the problem. For the storage monitoring framework, see ML storage IOPS queue monitoring. For the storage architecture, see AI storage architecture requirements.

Tracing the Data Path

The data path from storage to GPU has several hops: the storage endpoint, the storage network, the compute node's network interface, and the GPU memory. Latency at any hop slows the entire path, and the symptom — GPUs waiting for data — looks the same regardless of which hop is slow. Tracing measures latency per hop: endpoint read latency, network transit latency, node ingest latency, and GPU memory transfer latency. The hop with the largest contribution or the highest variance is the bottleneck. For how to correlate with GPU utilization, see ML storage IOPS monitoring.

Common Bottlenecks Found by Tracing

Storage endpoint saturation: the storage system's IOPS or throughput is maxed out, and all requests queue at the endpoint. Network congestion: the storage network is oversubscribed, and latency spikes during concurrent access. Client-side throttling: the data loader on the compute node cannot issue I/O fast enough because of CPU or memory limits. Metadata overload: listing millions of files overwhelms the filesystem's metadata server, and every file open is slow. Each has a different fix — adding storage throughput, upgrading the network, optimizing the data loader, or restructuring the dataset layout.

FAQ

How do I trace AI storage latency?

Measure latency at each hop of the data path: storage endpoint read latency, network transit, node ingest, and GPU memory transfer. The hop with the highest latency or variance is the bottleneck. Correlate with GPU idle time to confirm storage is the cause. See the method above.

Summary

AI storage latency tracing localizes the bottleneck by measuring latency at each data path hop. For the full monitoring framework, see ML storage IOPS monitoring.