There are two main ways to install the Futhark compiler: using a precompiled tarball or compiling from source. Both methods are discussed below. If you are using Linux, see Installing Futhark on Linux. If you are using Windows, make sure to read Setting up Futhark on Windows. If you are using macOS, read Using OpenCL or CUDA.
Futhark is also available via Nix. If you
are using Nix, simply install the
futhark derivation from Nixpkgs.
On non-Windows, you will need to have the
libraries installed. These are pretty common, so you may already have
them. On Debian-like systems (e.g. Ubuntu), use:
sudo apt install libtinfo-dev libgmp-dev
If you install Futhark via a package manager (e.g. Homebrew, Nix, or AUR), you shouldn’t need to worry about this.
1.2. 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
You can either retrieve a source release tarball or 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. This step
typically requires at least 8GiB of memory. You may be able to build
it on a smaller machine by adding the
--fast option, although the
resulting Futhark compiler binary will run slower.
After building, you can copy the binaries to your
directory by running:
$ stack install
Note that this does not install the Futhark manual pages.
1.3. 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).
See the enclosed
README.md for installation instructions.
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
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.
1.4. Installing Futhark on Linux¶
Linuxbrew is a distribution-agnostic package manager that contains a formula for Futhark. If Linuxbrew is installed (which does not require
rootaccess), installation is as easy as:
$ brew install futhark
Note that as of this writing, Linuxbrew is hampered by limited compute resources for building packages, so the Futhark version may be a bit behind.
Arch Linux users can use a futhark-nightly package.
Otherwise (or if the version in the package system is too old), your best bet is to install from source or use a tarball, as described above.
1.4.1. Using OpenCL or CUDA¶
If you wish to use
futhark opencl or
futhark cuda, you must
have the OpenCL or CUDA libraries installed, respectively. Consult
your favourite search engine for instructions on how to do this on
your distribution. It is usually not terribly difficult if you
already have working GPU drivers.
For OpenCL, note that there is a distinction between the general
OpenCL host library (
OpenCL.so) that Futhark links against, and
the Installable Client Driver (ICD) that OpenCL uses to actually
talk to the hardware. You will need both. Working display drivers
for the GPU does not imply that an ICD has been installed - they are
usually in a separate package. Consult your favourite search engine
1.5. Installing Futhark on macOS¶
Futhark is available on Homebrew, and the latest release can be installed via:
$ brew install futhark
Or you can install the unreleased development version with:
$ brew install --HEAD futhark
This has to compile from source, so it takes a little while (20-30 minutes is common).
macOS ships with one OpenCL platform and various devices. One of
these devices is always the CPU, which is not fully functional, and is
never picked by Futhark by default. You can still select it manually
with the usual mechanisms (see Executable Options), but it is
unlikely to be able to run most Futhark programs. Depending on the
system, there may also be one or more GPU devices, and Futhark will
simply pick the first one as always. On multi-GPU MacBooks, this is
is the low-power integrated GPU. It should work just fine, but you
might have better performance if you use the dedicated GPU instead.
On a Mac with an AMD GPU, this is done by passing
-dAMD to the
generated Futhark executable.
1.6. Setting up Futhark on Windows¶
The Futhark compiler itself is easily installed on Windows via
stack (see above). If you are using the default Windows console,
you may need to run
chcp 65001 to make Unicode characters show up
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
Github Desktop is optional and does not come with the required
1.6.1. Setting up Futhark and OpenCL¶
Clone the Futhark repository to your hard drive.
For editing environment variables it is strongly recommended that you install the Rapid Environment Editor
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 mingwpycommand.
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\binto the system-wide
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 gccin the command prompt should list the aforementioned MingWpy installation at the top or show only it.
To finish the installation, add the
CPATHenvironment variable (note: not
PATH). Create the variable if necessary.
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\includeand copy the
CLdirectory into the MingWpy
Next, go to
C:\Program Files\NVIDIA Corporation\OpenCLand copy the
OpenCL64.dllfile into the MingWpy
libdirectory (it is next to
The CUDA distribution also comes with the static
OpenCL.lib, but trying to use that one instead of the
OpenCL64.dllwill cause programs compiled with
futhark openclto crash, so ignore it completely.
Now you should be able to compile with
futhark opencl and run
Futhark programs on the GPU.
1.6.2. Setting up PyOpenCL¶
The following instructions are for how to setup the
First install Mako using
pip install mako.
Also install PyPNG using
pip install pypng (not stricly necessary,
but some examples make use of it).
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.
If you have ignored the instructions and gotten Python 3.x instead 2.7, you will have to do some extra work.
.\pyopencl\compyte\ndarray\test_gpu_ndarray.pyand convert most Python 2.x style print statements to Python 3 syntax. Basically wrap print arguments in brackets “(..)” and ignore any lines containing StringIO
Otherwise just go to the next point.
Go into the repo directory and from the command line execute
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.
Lastly, Pygame needs to be installed. Again, not stricly necessary, but some examples make use of it. To do so on Windows, download
cp27means Python 2.7 and
win_amd64means 64-bit Windows.
Go to the directory you have downloaded the file and execute
pip install pygame-1.9.2a0-cp27-none-win_amd64.whlfrom the command line.
Now you should be able to run the Game of Life example.
To run the makefiles, first setup
makeby going to the
bindirectory of MingWpy and making a copy of
mingw32-make.exe. Then simply rename
mingw32-make – Copy.exeor 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
Git for Windowsinstallation, add the
C:\Program Files\Git\usr\bindirectory to
This guide has been written off memory, so if you are having difficulties - ask on the issues page. There might be errors in it.