system-profile

To find bottlenecks in a GPU training process, you profile the workload to measure CPU overhead, GPU utilization, memory usage, and interconnect latency, generating a structured report with ranked recommendations.

Performance profiling diagnoses memory and CPU issues by measuring execution overhead and resource utilization across system layers, then turning the captured metrics into actionable next steps to fix inefficiencies.

Yes, you can profile interconnect latency and memory usage for running processes by applying external profilers to live workloads, capturing transfer characteristics and resource behavior without halting execution.

You do not always need code instrumentation to analyze GPU utilization; the profiling approach uses available external tools first and only adds minimal targeted instrumentation to capture missing signals.

The best way to generate a performance report for framework serving workloads is to profile the end-to-end service execution, producing structured performance tables and saving profiling artifacts for review.