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.

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

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.

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

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

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.

optimize-julia-code