https://github.com/jagot/threadedsparsearrays.jl

https://github.com/jagot/threadedsparsearrays.jl

Last synced: about 2 months ago
JSON representation

• Host: GitHub
• URL: https://github.com/jagot/threadedsparsearrays.jl
• Owner: jagot
• License: mit
• Created: 2020-03-23T20:31:54.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2023-12-27T17:16:45.000Z (about 1 year ago)
• Last Synced: 2024-08-09T22:41:58.512Z (5 months ago)
• Language: Julia
• Size: 29.3 KB
• Stars: 17
• Watchers: 2
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# ThreadedSparseArrays.jl

[![Build Status](https://github.com/jagot/ThreadedSparseArrays.jl/workflows/CI/badge.svg)](https://github.com/jagot/ThreadedSparseArrays.jl/actions)

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

Simple package providing a wrapper type enabling threaded sparse

matrix–dense matrix multiplication. Based on [this

PR](https://github.com/JuliaLang/julia/pull/29525).

## Installation

Install with:

```

] add ThreadedSparseArrays

```

Note that you *must* enable threading in Julia for

ThreadedSparseArrays to work. You can do so by setting the

[JULIA_NUM_THREADS](https://docs.julialang.org/en/v1/manual/environment-variables/#JULIA_NUM_THREADS-1)

environment variable. To test that it is set properly, run

```julia

Threads.nthreads()

```

and make sure it returns the number of threads you wanted.

## Example usage

To use ThreadedSparseArrays, all you need to do is to wrap your sparse

matrix using the ThreadedSparseMatrixCSC type, like this:

```julia

using SparseArrays

using ThreadedSparseArrays

A = sprand(10000, 100, 0.05); # sparse

X1 = randn(100, 100); # dense

X2 = randn(10000, 100); # dense

At = ThreadedSparseMatrixCSC(A); # threaded version

# threaded sparse matrix–dense matrix multiplication

B1 = At*X1;

B2 = At'X2;

```

## Notes

* If the right hand side `X` is a `Vector`, you need to use `At'X` to

get threading. `At*X` will not work.

* You might only get speedups for large matrices. Use `@btime` from

the [BenchmarkTools.jl](https://github.com/JuliaCI/BenchmarkTools.jl)

package to check if your use case is improved.