---

name: refactoring-09-performance-profiling description: Use when profiling and making small performance improvements in Python research code.

Refactoring 09: Performance and Bottlenecks

Goal

Find hotspots with profiling and apply small, safe optimizations.

Sequence

• Order: 09
• Previous: refactoring-08-experiment-tracking
• Next: refactoring-10-security-privacy

Workflow

• Reproduce a slow path with a small, representative dataset.
  • Success: A repeatable slow case is identified.
• Profile with a basic tool (cProfile, pyinstrument, or line_profiler).
  • Success: Hotspots are measured and recorded.
• Apply minimal fixes (vectorization, caching, avoiding repeated IO).
  • Success: Targeted code changes are implemented.
• Re-measure and record before/after metrics.
  • Success: Performance improvement is quantified.
• Keep optimizations local and reversible.
  • Success: Changes are isolated and easy to revert.

Guardrails

• Avoid algorithm changes unless requested.
• Do not optimize without measurements.
• Keep performance work separate from refactor-only changes.