- Working C and C++ toolchains(compiler, linker)
- cmake
- make or ninja
git clone --recursive https://github.com/llvm/Polygeist
cd PolygeistPolygeist can be built by providing paths to a pre-built MLIR and Clang toolchain.
- Build LLVM, MLIR, and Clang:
mkdir llvm-project/build
cd llvm-project/build
cmake -G Ninja ../llvm \
-DLLVM_ENABLE_PROJECTS="mlir;clang" \
-DLLVM_TARGETS_TO_BUILD="host" \
-DLLVM_ENABLE_ASSERTIONS=ON \
-DCMAKE_BUILD_TYPE=DEBUG
ninja
ninja check-mlirTo enable compilation to cuda add -DMLIR_ENABLE_CUDA_RUNNER=1 and remove -DLLVM_TARGETS_TO_BUILD="host" from the cmake arguments. (You may need to specify CUDACXX, CUDA_PATH, and/or -DCMAKE_CUDA_COMPILER)
To enable the ROCM backend add -DMLIR_ENABLE_ROCM_RUNNER=1 and remove -DLLVM_TARGETS_TO_BUILD="host" from the cmake arguments. (You may need to specify -DHIP_CLANG_INCLUDE_PATH, and/or ROCM_PATH)
For faster compilation we recommend using -DLLVM_USE_LINKER=lld.
- Build Polygeist:
mkdir build
cd build
cmake -G Ninja .. \
-DMLIR_DIR=$PWD/../llvm-project/build/lib/cmake/mlir \
-DCLANG_DIR=$PWD/../llvm-project/build/lib/cmake/clang \
-DLLVM_TARGETS_TO_BUILD="host" \
-DLLVM_ENABLE_ASSERTIONS=ON \
-DCMAKE_BUILD_TYPE=DEBUG
ninja
ninja check-polygeist-opt && ninja check-cgeistFor faster compilation we recommend using -DPOLYGEIST_USE_LINKER=lld.
1. GPU backends
To enable the CUDA backend add -DPOLYGEIST_ENABLE_CUDA=1
To enable the ROCM backend add -DPOLYGEIST_ENABLE_ROCM=1
2. Polymer
To enable polymer, add -DPOLYGEIST_ENABLE_POLYMER=1
This will cause the cmake invokation to pull and build the dependencies for polymer. To specify a custom directory for the dependencies, specify -DPOLYMER_DEP_DIR=<absolute-dir>. The dependencies will be build using the tools/polymer/build_polymer_deps.sh.
To run the polymer tests, use ninja check-polymer.
Polygeist can also be built as an external LLVM project using LLVM_EXTERNAL_PROJECTS.
- Build LLVM, MLIR, Clang, and Polygeist:
mkdir build
cd build
cmake -G Ninja ../llvm-project/llvm \
-DLLVM_ENABLE_PROJECTS="clang;mlir" \
-DLLVM_EXTERNAL_PROJECTS="polygeist" \
-DLLVM_EXTERNAL_POLYGEIST_SOURCE_DIR=.. \
-DLLVM_TARGETS_TO_BUILD="host" \
-DLLVM_ENABLE_ASSERTIONS=ON \
-DCMAKE_BUILD_TYPE=DEBUG
ninja
ninja check-polygeist-opt && ninja check-cgeistninja check-polygeist-opt runs the tests in Polygeist/test/polygeist-opt
ninja check-cgeist runs the tests in Polygeist/tools/cgeist/Test
If you use Polygeist, please consider citing the relevant publications:
@inproceedings{polygeistPACT,
title = {Polygeist: Raising C to Polyhedral MLIR},
author = {Moses, William S. and Chelini, Lorenzo and Zhao, Ruizhe and Zinenko, Oleksandr},
booktitle = {Proceedings of the ACM International Conference on Parallel Architectures and Compilation Techniques},
numpages = {12},
location = {Virtual Event},
series = {PACT '21},
publisher = {Association for Computing Machinery},
year = {2021},
address = {New York, NY, USA},
keywords = {Polygeist, MLIR, Polyhedral, LLVM, Compiler, C++, Pluto, Polly, OpenScop, Parallel, OpenMP, Affine, Raising, Transformation, Splitting, Automatic-Parallelization, Reduction, Polybench},
}
@inproceedings{10.1145/3572848.3577475,
author = {Moses, William S. and Ivanov, Ivan R. and Domke, Jens and Endo, Toshio and Doerfert, Johannes and Zinenko, Oleksandr},
title = {High-Performance GPU-to-CPU Transpilation and Optimization via High-Level Parallel Constructs},
year = {2023},
isbn = {9798400700156},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3572848.3577475},
doi = {10.1145/3572848.3577475},
booktitle = {Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming},
pages = {119–134},
numpages = {16},
keywords = {MLIR, polygeist, CUDA, barrier synchronization},
location = {Montreal, QC, Canada},
series = {PPoPP '23}
}
@inproceedings{10444828,
author = {Ivanov, Ivan R. and Zinenko, Oleksandr and Domke, Jens and Endo, Toshio and Moses, William S.},
booktitle = {2024 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)},
title = {Retargeting and Respecializing GPU Workloads for Performance Portability},
year = {2024},
volume = {},
issn = {},
pages = {119-132},
doi = {10.1109/CGO57630.2024.10444828},
url = {https://doi.ieeecomputersociety.org/10.1109/CGO57630.2024.10444828},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month = {mar}
}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent