system-profile

To find performance bottlenecks, you profile the running process or service to measure CPU overhead, memory usage, GPU compute, and interconnect communication. This skill selects external tools or writes targeted instrumentation to capture structured metrics.

Targeted instrumentation is custom code inserted into a workload when external profiling tools are insufficient. You need it to capture granular performance data for specific hardware components like GPU or interconnect, generating a detailed changelog.

Yes, you can profile GPU compute and interconnect communication for Python scripts and modules. The profiling process selects appropriate external tooling, runs the analysis, and saves output artifacts under a designated profile_output directory.

The best way to profile end-to-end framework execution is using a hybrid approach that selects appropriate external tools and inserts custom instrumentation code. This generates a structured report ranking CPU, memory, GPU, and interconnect performance issues.

Profiling memory redundancy and CPU overhead does not require specific dependencies, as the skill selects appropriate external tooling dynamically. It diagnoses inefficiencies by choosing suitable profilers based on the specified target.