optimize-julia-code

name	optimize-julia-code
description	Measure and improve Julia runtime performance with BenchmarkTools, profiling and allocation tools, inference diagnosis, data-layout changes, and guarded low-level optimizations. Use when Julia code is slow, allocation-heavy, type-unstable, or needs a reproducible performance baseline or regression check.

Optimize Julia Code

Optimize an observed workload, preserve its behavior, and remeasure every change.

Workflow

1. Define representative inputs and record a correct baseline.
2. Establish repeatable timing and allocation measurements. Read measurement-and-benchmarks.md.
3. Locate time or allocation hotspots before editing. Read profiling-and-allocations.md.
4. Inspect inference and memory access only where measurements point. Read inference-and-data-layout.md.
5. Apply annotations, concurrency, or specialized tooling only after simpler changes. Read advanced-optimization.md.
6. Run correctness tests and the original measurement after each focused change. Keep only improvements that hold on representative inputs.

Boundaries

• Separate first-call latency from steady-state runtime.
• Prefer algorithmic and data-movement improvements over syntax-level tweaks.
• Do not make argument types concrete merely for speed; Julia specializes on actual argument types.
• Do not add an optimization package without a measured bottleneck, a current compatibility check, and a benchmark against Base Julia.
• Report the workload, Julia version, thread configuration, inputs, and measurement method with performance conclusions.