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README
LLVM backends 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.
Dependencies
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 theaccelerate-llvm-native
backend for multicore CPUs)CUDA
(if using theaccelerate-llvm-ptx
backend for NVIDIA GPUs)
Docker
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
docker plugin; see that page for more
information.
$ 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.
Homebrew
Example using Homebrew on macOS:
$ brew install llvm-hs/llvm/llvm-9
Debian/Ubuntu
For Debian/Ubuntu based Linux distributions, the LLVM.org website provides binary distribution packages. Check apt.llvm.org for instructions for adding the correct package database for your OS version, and then:
$ apt-get install llvm-9-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 LLVM.org 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).
Download and unpack the LLVM-9 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.Create a temporary build directory and
cd
into it, for example:$ mkdir /tmp/build $ cd /tmp/build
Execute the following to configure the build. Here
INSTALL_PREFIX
is where LLVM is to be installed, for example/usr/local
or$HOME/opt/llvm
:$ cmake $LLVM_SRC -DCMAKE_INSTALL_PREFIX=$INSTALL_PREFIX -DCMAKE_BUILD_TYPE=Release -DLLVM_ENABLE_ASSERTIONS=ON -DLLVM_BUILD_LLVM_DYLIB=ON -DLLVM_LINK_LLVM_DYLIB=ON
See options and variables for a list of additional build parameters you can specify.
Build and install:
$ cmake --build . $ cmake --build . --target install
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-9.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-8.8.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 9.0.
libNVVM
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
combinations which have been tested:
LLVM-3.3 | LLVM-3.4 | LLVM-3.5 | LLVM-3.8 | LLVM-3.9 | LLVM-4.0 | LLVM-5.0 | LLVM-6.0 | LLVM-7 | LLVM-8 | LLVM-9 | |
---|---|---|---|---|---|---|---|---|---|---|---|
CUDA-7.0 | ⭕ | ❌ | |||||||||
CUDA-7.5 | ⭕ | ⭕ | ❌ | ||||||||
CUDA-8.0 | ⭕ | ⭕ | ❌ | ❌ | |||||||
CUDA-9.0 | ❌ | ❌ | |||||||||
CUDA-9.1 | |||||||||||
CUDA-9.2 | |||||||||||
CUDA-10.0 | |||||||||||
CUDA-10.1 |
Where ⭕ = Works, and ❌ = Does not work.
The above table is incomplete! If you try a particular combination and find that it does or does not work, please let us know!
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-4.0.
Using stack
, either edit the stack.yaml
and add the following section:
flags:
accelerate-llvm-ptx:
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