performance-assassin_lc_v1

Profile your Python module using deterministic tooling to locate CPU hotspots and memory leaks during development. Apply multi-phase profiling across module-level analysis, micro-benchmarks, and memory audits to identify slow paths.

Run micro-benchmarks using pytest-benchmark to validate performance improvements before shipping. This approach applies deterministic tests to measure and audit slow paths within your development cycle iteratively.

Yes, you need scalene, pyinstrument, and memory_profiler set up in your project. The profiling workflow requires these deterministic tools, alongside tracemalloc and optional memray, to execute end-to-end memory analysis.

Yes, you can use tracemalloc and memory_profiler for module-level performance audits. They enable multi-phase memory analysis to locate memory leaks and scalability hotspots during development before shipping code.

Load testing applies stress-testing to identify slow paths and hidden performance regressions. By applying deterministic tooling and tests, it validates performance improvements and exposes scalability hotspots across critical modules.