
Use when profiling Linux Python or C workloads for algorithmic scaling, cache, branch, memory, or ASM bottlenecks, or when comparing a benchmark run against a saved performance baseline.
Evidence-driven performance optimization that consumes perf-benchmark findings, selects one bounded candidate, re-measures with identical profiling, and records accepted wins or honest no-win outcomes.