1. Installation

There are two ways to install the Futhark compiler: using a precompiled tarball or compiling from source. Both methods are discussed below. If you are using Windows, make sure to read Installing Futhark on Windows.

1.1. Compiling from source

We use the the Haskell Tool Stack to handle dependencies and compilation of the Futhark compiler, so you will need to install the stack tool. Fortunately, the stack developers provide ample documentation about installing Stack on a multitude of operating systems. If you’re lucky, it may even be in your local package repository.

We do not presently issue source releases of Futhark, so the only way to compile from source is to perform a checkout of our Git repository:

$ git clone https://github.com/diku-dk/futhark.git

This will create a directory futhark, which you must enter:

$ cd futhark

To get all the prerequisites for building the Futhark compiler (including, if necessary, the appropriate version of the Haskell compiler), run:

$ stack setup

Note that this will not install anything system-wide and will have no effect outside the Futhark build directory. Now you can run the following command to build the Futhark compiler, including all dependencies:

$ stack build

The Futhark compiler and its tools will now be built. You can copy them to your $HOME/.local/bin directory by running:

$ stack install

Note that this does not install the Futhark manual pages.

1.2. Installing from a precompiled snapshot

Tarballs of binary releases can be found online, but are available only for very few platforms (as of this writing, only GNU/Linux on x86_64).

Furthermore, every day a program automatically clones the Git repository, builds the compiler, and packages a simple tarball containing the resulting binaries, built manpages, and a simple Makefile for installing. The implication is that these tarballs are not vetted in any way, nor more stable than Git HEAD at any particular moment in time. They are provided for users who wish to use the most recent code, but are unable to compile Futhark themselves.

At the moment, we build such snapshots only for a single operating system:

Linux (x86_64)

In time, we hope to make snapshots available for more platforms, but we are limited by system availability.

1.3. Installing Futhark on Windows

While the Futhark compiler itself is easily installed on Windows via stack (see above), it takes a little more work to make the OpenCL and PyOpenCL backends functional. This guide was last updated on the 5th of May 2016, and is for computers using 64-bit Windows along with CUDA 7.5 and Python 2.7 (Anaconda preferred).

Also Git for Windows is required for its Linux command line tools. If you have not marked the option to add them to path, there are instructions below how to do so. The GUI alternative to git, Github Desktop is optional and does not come with the required tools.

1.3.1. Setting up Futhark and OpenCL

  1. Clone the Futhark repository to your hard drive.

  2. Install Stack using the 64-bit installer. Compile the Futhark compiler as described in Installation.

  3. For editing environment variables it is strongly recommended that you install the Rapid Environment Editor

  4. For a Futhark compatible C/C++ compiler, that you will also need to install pyOpenCL later, install MingWpy. Do this using the pip install -i https://pypi.anaconda.org/carlkl/simple mingwpy command.

  5. Assuming you have the latest Anaconda distribution as your primary one, it will get installed to a place such as C:\Users\UserName\Anaconda2\share\mingwpy. The pip installation will not add its bin or include directories to path.

    To do so, open the Rapid Environment Editor and add C:\Users\UserName\Anaconda2\share\mingwpy\bin to the system-wide PATH variable.

    If you have other MingW or GCC distributions, make sure MingWpy takes priority by moving its entry above the other distributions. You can also change which Python distribution is the default one using the same trick should you need so.

    If have done so correctly, typing where gcc in the command prompt should list the aforementioned MingWpy installation at the top or show only it.

    To finish the installation, add the C:\Users\UserName\Anaconda2\share\mingwpy\include to the CPATH environment variable (note: not PATH). Create the variable if necessary.

  6. The header files and the .dll for OpenCL that comes with the CUDA 7.5 distribution also need to be installed into MingWpy. Go to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5\include and copy the CL directory into the MingWpy include directory.

    Next, go to C:\Program Files\NVIDIA Corporation\OpenCL and copy the OpenCL64.dll file into the MingWpy lib directory (it is next to include).

    The CUDA distribution also comes with the static OpenCL.lib, but trying to use that one instead of the OpenCL64.dll will cause programs compiled with futhark-opencl to crash, so ignore it completely.

Now you should be able to compile futhark-opencl and run Futhark programs on the GPU.


1.3.2. Setting up PyOpenCL

The following instructions are for how to setup the futhark-pyopencl backend.

First install Mako using pip install mako.

Also install PyPNG using pip install pypng (not stricly necessary, but some examples make use of it).

  1. Clone the PyOpenCL repository to your hard drive. Do this instead of downloading the zip, as the zip will not contain some of the other repositories it links to and you will end up with missing header files.

  2. If you have ignored the instructions and gotten Python 3.x instead 2.7, you will have to do some extra work.

    Edit .\pyopencl\compyte\ndarray\gen_elemwise.py and .\pyopencl\compyte\ndarray\test_gpu_ndarray.py and convert most Python 2.x style print statements to Python 3 syntax. Basically wrap print arguments in brackets “(..)” and ignore any lines containing StringIO >> operator.

    Otherwise just go to the next point.

  3. Go into the repo directory and from the command line execute python configure.py.

    Edit siteconf.py to following:

    CL_TRACE = false
    CL_ENABLE_GL = false
    CL_INC_DIR = ['c:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v7.5\\include']
    CL_LIB_DIR = ['C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v7.5\\lib\\x64']
    CL_LIBNAME = ['OpenCL']
    CXXFLAGS = ['-std=c++0x']
    LDFLAGS = []

    Run the following commands:

    > python setup.py build_ext --compiler=mingw32
    > python setup.py install

If everything went in order, pyOpenCL should be installed on your machine now.

  1. Lastly, Pygame needs to be installed. Again, not stricly necessary, but some examples make use of it. To do so on Windows, download pygame-1.9.2a0-cp27-none-win_amd64.whl from here. cp27 means Python 2.7 and win_amd64 means 64-bit Windows.

    Go to the directory you have downloaded the file and execute pip install pygame-1.9.2a0-cp27-none-win_amd64.whl from the command line.

Now you should be able to run the Mandelbrot Explorer and and Game of Life examples.

  1. To run the makefiles, first setup make by going to the bin directory of MingWpy and making a copy of mingw32-make.exe. Then simply rename mingw32-make Copy.exe or similar to make.exe. Now you will be able to run the makefiles.

    Also, if you have not selected to add the optional Linux command line tools to PATH during the Git for Windows installation, add the C:\Program Files\Git\usr\bin directory to PATH manually now.

  2. This guide has been written off memory, so if you are having difficulties - ask on the issues page. There might be errors in it.