performance-investigation

To find latency causes, establish a systematic performance investigation loop to measure baseline wall, user, and system time. Then use profilers like samply or perf to generate flamegraphs that distinguish between compute-bound on-CPU costs and off-CPU wait times.

The best way to profile a slow binary is to use a data-driven investigation loop that quantifies bottlenecks before applying fixes. Use profiling tools like samply or cargo-flamegraph to generate actionable flamegraphs that visualize CPU and off-CPU activity.

A flamegraph helps with software optimization by visualizing CPU and off-CPU activity to localize exact execution bottlenecks. It reveals whether delays stem from inefficient compute code or external system overhead like IO, locks, or subprocess churn.

Yes, performance investigation works for off-CPU wait times by distinguishing them from on-CPU compute costs. It identifies delays caused by external system overhead like IO operations, locks, or subprocess churn using targeted profiling metrics.

Yes, you need standard system timing utilities to debug performance issues and capture baseline wall, user, and system time. Standardizing these measurements establishes a rigorous, data-driven loop to quantify bottlenecks before applying fixes.

Your CLI tool runs slow with low CPU usage because of off-CPU wait times caused by IO, locks, or subprocess churn. Profiling with tools like samply distinguishes these external system overhead delays from actual compute-bound tasks.