Added usage instructions

Author: rasmushenningsson

README.md

@@ -1,4 +1,4 @@
-# ThreadedSparseArrays
+# ThreadedSparseArrays.jl
 
 [![Build Status](https://travis-ci.com/jagot/ThreadedSparseArrays.jl.svg?branch=master)](https://travis-ci.com/jagot/ThreadedSparseArrays.jl)
 [![Build Status](https://ci.appveyor.com/api/projects/status/github/jagot/ThreadedSparseArrays.jl?svg=true)](https://ci.appveyor.com/project/jagot/ThreadedSparseArrays-jl)
@@ -7,3 +7,45 @@
 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
+ThreadedSparseArrays.jl is not yet a registered package, but you can
+install it with:
+```
+] add [email protected]:jagot/ThreadedSparseArrays.jl.git
+```
+
+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. `A*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.