julia-bench-quick

Benchmark Julia code snippets with the BenchmarkTools.jl @btime macro.

30|6|Updated Jan 21, 2026

Search Tags:#development #performance #optimization #benchmarking #julia #@btime

Ready to run

npx skills add https://github.com/Krastanov/JuliaLLMAgentSkills --skill julia-bench-quick

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: julia-bench-quick
Source: https://github.com/Krastanov/JuliaLLMAgentSkills/tree/main/julia-bench-quick
Command: npx skills add https://github.com/Krastanov/JuliaLLMAgentSkills --skill julia-bench-quick

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps developers quickly measure the performance of small Julia code snippets during development, identifying performance bottlenecks early.

Core Features & Use Cases

Ad-hoc Benchmarking: Use the @btime macro for immediate performance feedback.
Setup for Mutating Functions: Ensure accurate benchmarks for functions that modify their input.
Allocation Measurement: Understand memory allocation overhead.
Use Case: While writing a new function, you can use this skill to instantly check if a particular implementation is faster than another.

Quick Start

Use the julia-bench-quick skill to benchmark the sum of a random vector v using @btime sum($v).

Frequently Asked Questions about julia-bench-quick

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I run quick Julia benchmarks for code snippets?▼

Run quick Julia benchmarks by executing code snippets with the BenchmarkTools.jl `@btime` macro to get immediate performance feedback and measure memory allocations during development.

How does interpolation with `$` affect Julia benchmarking results?▼

Interpolation with `$` in Julia benchmarking isolates variable setup from the timed execution, ensuring the macro measures only the function's runtime and not the cost of evaluating the variable itself.

How do I benchmark mutating functions in Julia without skewing the results?▼

Benchmark mutating functions in Julia by using setup phases within the `@btime` macro to reset input data before each trial, ensuring accurate timing for operations that modify their arguments.

Can I use `@btime` to measure memory allocation overhead in Julia?▼

Yes, you can use `@btime` to measure memory allocation overhead in Julia, as the macro reports the number of bytes allocated during execution alongside the minimum elapsed time.

What's the best way to compare two Julia function implementations for speed?▼

The best way to compare Julia function implementations is by running ad-hoc benchmarks with `@btime` to instantly measure execution time and memory allocations side-by-side during development.

Why does my Julia benchmark include compilation time in the results?▼

Julia benchmarks include compilation time if interpolation with `$` is not used for variables, causing the macro to evaluate setup code repeatedly during timing instead of isolating the compiled function.