Elaborate on installing MKL in readme

Author: Crown421

README.md

@@ -10,21 +10,31 @@ Similar packages are [Yeppp.jl](https://github.com/JuliaMath/Yeppp.jl), which wr
 
 ## Basic install
 
-To use IntelVectorMath.jl, you must have the shared libraries of the Intel Vector Math Library available on your system.
+To use IntelVectorMath.jl, you must have the shared libraries of the Intel Vector Math Library available on your system, for which you currently have two options:
+
+### 1. MKL.jl
 The easiest option is to use [MKL.jl](https://github.com/JuliaComputing/MKL.jl) via
 ```julia
 julia> ] add https://github.com/JuliaComputing/MKL.jl.git
 ```
-Alternatively you can install MKL directly [from intel](https://software.intel.com/en-us/mkl/choose-download).
+#### Note:
+This will overwrite your julia sysimage to use MKL instead of OpenBLAS which could break compatability with certain packages and is difficult to reverse without reinstalling julia from scratch. In addition, MKL.jl is currently broken on Windows, so you can only use the following option. Please see the MKL.jl repository for more information. 
+
+### 2. Standalone MKL
+You can also install MKL directly [from intel](https://software.intel.com/en-us/mkl/choose-download). For macOS and Windows this requires a free registration, on Linux this can be done via the command line, as seen [here](https://github.com/JuliaMath/IntelVectorMath.jl/blob/d4f8dd4083cf228cd493a4aed9964f1bc0f08d4f/.github/workflows/main.yml#L53).
+There is also the `intel-mkl-slim` package in the AUR that works well. 
 
 Note that intel MKL has a separate license, which you may want to check for commercial projects (see [FAQ]( https://software.intel.com/en-us/mkl/license-faq)).
 
+### IntelVectorMath
 To install IntelVectorMath.jl run
 ```julia
 julia> ] add IntelVectorMath
 ```
 If you used this package prior to its renaming, you may have to run `] rm VML` first. Otherwise there will be a conflict due to the UUID.  
 
+In the event that MKL was not installed properly you will get an error when first `using` it. 
+
 ## Using IntelVectorMath
 After loading `IntelVectorMath`, you have the supported function listed below, for example `IntelVectorMath.sin(rand(100))`. These should provide a significant speed-up over broadcasting the Base functions.
 Since the package name is quite long, an alias `IVM` is also exported to allow `IVM.sin(rand(100))` after `using` the package.