GPU execution is a technique for high-performance machine learning, financial, image processing and other data-parallel numerical programming. The following options are available for executing F# on the GPU.

If you would like to list an option here, please submit a pull request by editing this page.

Option 1 - Use Alea GPU V3, for F#-enabled CUDA programming

logo  Alea GPU is a GPU programming toolchain supporting

Alea GPU is a complete solution to develop CUDA accelerated GPU applications on .NET. It is a full compiler based on F# and LLVM to generate highly optimized GPU code. Alea GPU performs at the same level as CUDA C/C++ or Fortran code.

Alea TK is a new open source machine learning library for .NET based on Alea GPU, which shows how to use Alea GPU in larger projects.

Option 2 - Use Brahma.FSharp, an open source F# tool for OpenCL programming

Brahma.FSharp is a library for F# quotations to OpenCL translation.

Features of Brahma.FSharp:

Option 3 - Use FSCL, an open-source F#-to-OpenCL compiler

Option 4 - Use GpuLINQ, an open source F#/C# LINQ-to-OpenCL compiler