https://github.com/lilithhafner/chairmarks.jl

Benchmarks with back support
https://github.com/lilithhafner/chairmarks.jl

Last synced: 2 months ago
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Benchmarks with back support

• Host: GitHub
• URL: https://github.com/lilithhafner/chairmarks.jl
• Owner: LilithHafner
• License: gpl-3.0
• Created: 2023-11-11T01:13:15.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-15T18:58:29.000Z (3 months ago)
• Last Synced: 2025-02-15T19:34:32.786Z (3 months ago)
• Language: Julia
• Size: 30.7 MB
• Stars: 95
• Watchers: 2
• Forks: 9
• Open Issues: 11
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Chairmarks

[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://Chairmarks.lilithhafner.com/stable/)

[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://Chairmarks.lilithhafner.com/dev/)

[![Build Status](https://github.com/LilithHafner/Chairmarks.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/LilithHafner/Chairmarks.jl/actions/workflows/CI.yml?query=branch%3Amain)

[![Coverage](https://codecov.io/gh/LilithHafner/Chairmarks.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/LilithHafner/Chairmarks.jl)

[![Aqua](https://raw.githubusercontent.com/JuliaTesting/Aqua.jl/master/badge.svg)](https://github.com/JuliaTesting/Aqua.jl)

Chairmarks measures performance [hundreds of times faster](https://Chairmarks.lilithhafner.com/stable/why#efficient)

than BenchmarkTools [without compromising on accuracy](https://Chairmarks.lilithhafner.com/stable/why#precise).

Installation

```julia

julia> import Pkg; Pkg.add("Chairmarks")

```

Usage

```julia

julia> using Chairmarks

julia> @b rand(1000) # How long does it take to generate a random array of length 1000?

720.214 ns (3 allocs: 7.875 KiB)

julia> @b rand(1000) hash # How long does it take to hash that array?

1.689 μs

julia> @b rand(1000) _.*5 # How long does it take to multiply it by 5 element wise?

172.970 ns (3 allocs: 7.875 KiB)

julia> @b rand(100,100) inv,_^2,sum # Is it be faster to invert, square, or sum a matrix? [THIS USAGE IS EXPERIMENTAL]

(92.917 μs (9 allocs: 129.203 KiB), 27.166 μs (3 allocs: 78.203 KiB), 1.083 μs)

```

[Tutorial](https://Chairmarks.lilithhafner.com/stable/tutorial)

[Why Chairmarks?](https://Chairmarks.lilithhafner.com/stable/why)

[API Reference](https://Chairmarks.lilithhafner.com/stable/reference)