https://github.com/nlw0/chipsort.jl

Sorting deeds done down the chip
https://github.com/nlw0/chipsort.jl

cache-aware meta-programming simd sorting

Last synced: 3 months ago
JSON representation

Sorting deeds done down the chip

• Host: GitHub
• URL: https://github.com/nlw0/chipsort.jl
• Owner: nlw0
• License: mit
• Created: 2019-02-10T20:17:57.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2023-08-15T16:19:05.000Z (over 2 years ago)
• Last Synced: 2025-03-10T22:43:56.513Z (about 1 year ago)
• Topics: cache-aware, meta-programming, simd, sorting
• Language: Julia
• Homepage:
• Size: 677 KB
• Stars: 23
• Watchers: 1
• Forks: 2
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# ChipSort.jl



  



ChipSort is a sorting module containing SIMD and cache-aware techniques. It's based on a couple of academic papers from 2008. More details can be found in [our documentation](https://nlw0.github.io/ChipSort.jl).

[![docs](https://img.shields.io/badge/docs-dev-blue.svg)](https://nlw0.github.io/ChipSort.jl/dev)

[![Build Status](https://app.travis-ci.com/nlw0/ChipSort.jl.svg?branch=master)](https://app.travis-ci.com/nlw0/ChipSort.jl)

[![codecov](https://codecov.io/gh/nlw0/ChipSort.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/nlw0/ChipSort.jl)

[![DOI](https://zenodo.org/badge/170022251.svg)](https://zenodo.org/badge/latestdoi/170022251)

## Installation and usage

Like any experimental Julia package on GitHub you can install ChipSort from the Julia REPL by first typing `]` to enter the package management prompt, and then

```

pkg> add https://github.com/nlw0/ChipSort.jl

```

You can now try out the basic functions offered by the package such as `sort_net` to use a sorting network, or try the full array sort function prototype `chipsort`.

```

julia> using ChipSort

julia> using SIMD

julia> data = [Vec(tuple(rand(Int8, 4)...)) for _ in 1:4]

4-element Array{Vec{4,Int8},1}:

 <4 x Int8>[-15, 98, 5, -28]

 <4 x Int8>[47, -112, 98, -14]

 <4 x Int8>[-18, -3, -111, 85]

 <4 x Int8>[79, -12, -44, -85]

julia> x = sort_net(data...)

(<4 x Int8>[-18, -112, -111, -85], <4 x Int8>[-15, -12, -44, -28], <4 x Int8>[47, -3, 5, -14], <4 x Int8>[79, 98, 98, 85])

julia> y = transpose_vecs(x...)

(<4 x Int8>[-18, -15, 47, 79], <4 x Int8>[-112, -12, -3, 98], <4 x Int8>[-111, -44, 5, 98], <4 x Int8>[-85, -28, -14, 85])

julia> z = merge_vecs(y...)

<16 x Int8>[-112, -111, -85, -44, -28, -18, -15, -14, -12, -3, 5, 47, 79, 85, 98, 98]

julia> bigdata = rand(Int32, 2^20);

julia> chipsort_large(bigdata, Val(8), Val(32)) == sort(bigdata)

true

```

Make sure you check [our documentation](https://nlw0.github.io/ChipSort.jl) for more information.

Latest [benchmark](https://gist.github.com/nlw0/a4180e7d131bf127cd738f5ad2da284c) results are: 81% speedup on a 1M Int32 array, 2x speedup on 8k Int32 and 4x on 64 values.