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Short Description
An LLVM backend for the Accelerate array language
Full Description

An LLVM backend for the Accelerate Array Language

This package compiles Accelerate code to LLVM IR, and executes that code on
multicore CPUs as well as NVIDIA GPUs. This avoids the need to go through nvcc
or clang. For details on Accelerate, refer to the main repository.

We love all kinds of contributions, so feel free to open issues for missing
features as well as report (or fix!) bugs on the issue tracker.


Haskell dependencies are available from Hackage, but there are several external
library dependencies that you will need to install as well:

  • LLVM
  • libFFI (if using the accelerate-llvm-native backend for multicore CPUs)
  • CUDA (if using the accelerate-llvm-ptx backend for NVIDIA GPUs)


A docker container is provided with this package
preinstalled (via stack) at /opt/accelerate-llvm. Note that if you wish to use
the accelerate-llvm-ptx GPU backend, you will need to install the NVIDIA
plugin; see that page for more

$ docker run -it tmcdonell/accelerate-llvm

Installing LLVM

When installing LLVM, make sure that it includes the libLLVM shared library.
If you want to use the GPU targeting accelerate-llvm-ptx backend, make sure
you install (or build) LLVM with the 'nvptx' target.


Example using Homebrew on macOS:

$ brew install llvm-hs/homebrew-llvm/llvm-4.0


For Debian/Ubuntu based Linux distributions, the website provides
binary distribution packages. Check for
instructions for adding the correct package database for your OS version, and

$ apt-get install llvm-4.0-dev

Building from source

If your OS does not have an appropriate LLVM distribution available, you can also build from source. Detailed build instructions are available on the website. Note that you will require at least CMake 3.4.3 and a recent C++ compiler; at least Clang 3.1, GCC 4.8, or Visual Studio 2015 (update 3).

  1. Download and unpack the LLVM-4.0 source code. We'll refer to
    the path that the source tree was unpacked to as LLVM_SRC. Only the main
    LLVM source tree is required, but you can optionally add other components
    such as the Clang compiler or Polly loop optimiser. See the LLVM releases
    page for the complete list.

  2. Create a temporary build directory and cd into it, for example:

    $ mkdir /tmp/build
    $ cd /tmp/build
  3. Execute the following to configure the build. Here INSTALL_PREFIX is
    where LLVM is to be installed, for example /usr/local or


    See options and variables
    for a list of additional build parameters you can specify.

  4. Build and install:

    $ cmake --build .
    $ cmake --build . --target install
  5. For macOS only, some additional steps are useful to work around issues related
    to System Integrity Protection:

    cd $INSTALL_PREFIX/lib
    ln -s libLLVM.dylib libLLVM-4.0.dylib
    install_name_tool -id $PWD/libLTO.dylib libLTO.dylib
    install_name_tool -id $PWD/libLLVM.dylib libLLVM.dylib
    install_name_tool -change '@rpath/libLLVM.dylib' $PWD/libLLVM.dylib libLTO.dylib

Installing Accelerate-LLVM

Once the dependencies are installed, we are ready to install accelerate-llvm.

For example, installation using stack
just requires you to point it to the appropriate configuration file:

$ ln -s stack-8.0.yaml stack.yaml
$ stack setup
$ stack install

Note that the version of llvm-hs
used must match the installed version of LLVM, which is currently 4.0.


The accelerate-llvm-ptx backend can optionally be compiled to generate GPU
code using the libNVVM library, rather than LLVM's inbuilt NVPTX code
generator. libNVVM is a closed-source library distributed as part of the
NVIDIA CUDA toolkit, and is what the nvcc compiler itself uses internally when
compiling CUDA C code.

Using libNVVM may improve GPU performance compared to the code generator
built in to LLVM. One difficulty with using it however is that since libNVVM
is also based on LLVM, and typically lags LLVM by several releases, you must
install accelerate-llvm with a "compatible" version of LLVM, which will depend
on the version of the CUDA toolkit you have installed. The following table shows
some combinations:

LLVM-3.3 LLVM-3.4 LLVM-3.5 LLVM-3.8 LLVM-3.9 LLVM-4.0

Where ⭕ = Works, and ❌ = Does not work.

Note that the above restrictions on CUDA and LLVM version exist only if you
want to use the NVVM component. Otherwise, you should be free to use any
combination of CUDA and LLVM.

Also note that accelerate-llvm-ptx itself currently requires at least LLVM-3.5.

Using stack, either edit the stack.yaml and add the following section:

    nvvm: true

Or install using the following option on the command line:

$ stack install accelerate-llvm-ptx --flag accelerate-llvm-ptx:nvvm

If installing via cabal:

$ cabal install accelerate-llvm-ptx -fnvvm
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