https://github.com/tkf/benchsweeps.jl

https://github.com/tkf/benchsweeps.jl

Last synced: 4 months ago
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• Host: GitHub
• URL: https://github.com/tkf/benchsweeps.jl
• Owner: tkf
• License: mit
• Created: 2018-12-08T18:08:45.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2020-03-16T03:59:57.000Z (about 6 years ago)
• Last Synced: 2025-05-24T09:38:37.586Z (about 1 year ago)
• Language: Julia
• Size: 17.6 KB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# BenchSweeps

[![Build Status](https://travis-ci.com/tkf/BenchSweeps.jl.svg?branch=master)](https://travis-ci.com/tkf/BenchSweeps.jl)

[![Codecov](https://codecov.io/gh/tkf/BenchSweeps.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/tkf/BenchSweeps.jl)

[![Coveralls](https://coveralls.io/repos/github/tkf/BenchSweeps.jl/badge.svg?branch=master)](https://coveralls.io/github/tkf/BenchSweeps.jl?branch=master)

BenchSweeps.jl wraps [BenchmarkTools.jl] to:

* Setup grid of parameters and define benchmarks for all combinations

  of them.  Those parameters and defined benchmarks are bundled

  together by `BenchSweepGroup`.

* Access benchmark data using dataframe/table-like interface.

  `BenchSweepGroup` supports [Tables.jl] and [IterableTables.jl] API

  and more controllable interface for [DataFrames.jl].

* Save/load `BenchSweepGroup` to/from a JSON file.

[BenchmarkTools.jl]: https://github.com/JuliaCI/BenchmarkTools.jl

[Tables.jl]: https://github.com/JuliaData/Tables.jl

[IterableTables.jl]: https://github.com/queryverse/IterableTables.jl

[DataFrames.jl]: https://github.com/JuliaData/DataFrames.jl

### Usage:

```julia

using BenchSweeps

using LinearAlgebra

suite = BenchSweepGroup()

suite.axes[:n] = 2 .^ (2:5)

suite.axes[:m] = 2 .^ (2:5)

@defsweep! for (n, m) in suite["matrix-matrix"]

    @benchmarkable mul!(Y, A, X) setup=begin

        Y = zeros($n, $m)

        A = rand($n, $n)

        X = rand($n, $m)

    end

end

results = run(suite)

using DataFrames

df = DataFrame(results)  # now analyze the benchmark

```