CuPBoP/README.md

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# CuPBoP: Cuda for Parallelized and Broad-range Processors
## Introduction
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CuPBoP is a framework which support executing unmodified CUDA source code
on non-NVIDIA devices.
Currently, CuPBoP support serveral CPU backends, including x86, AArch64, and RISC-V.
Supporting [Vortex](https://vortex.cc.gatech.edu/) (a RISC-V GPU) is working in progress.
## Install
### Prerequisites
- Linux system
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- [LLVM 14.0.1](https://github.com/llvm/llvm-project/releases/tag/llvmorg-14.0.1)
- CUDA Toolkit
Although CuPBoP does not require NVIDIA GPUs,
it needs CUDA to compile the source programs to NVVM/LLVM IRs.
CUDA toolkit can be built on machines without NVIDIA GPUs.
For building CUDA toolkit, please refer to <https://developer.nvidia.com/cuda-downloads>.
### Installation
1. Clone from github
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```bash
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git clone --recursive https://github.com/cupbop/CuPBoP
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cd CuPBoP
export CuPBoP_PATH=`pwd`
export LD_LIBRARY_PATH=$CuPBoP_PATH/build/runtime:$CuPBoP_PATH/build/runtime/threadPool:$LD_LIBRARY_PATH
export CUDA_PATH=/usr/local/cuda-11.7 # set to your own location
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```
2. Build CuPBoP
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```bash
mkdir build && cd build
#set -DDEBUG=ON for debugging
cmake .. \
-DLLVM_CONFIG_PATH=`which llvm-config` \
-DCUDA_PATH=$CUDA_PATH
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make
```
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3. (Optional) Use CuPBoP to execute Hetero-mark benchmark for verification
```bash
make test
```
## Run Vector Addition example
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In this section, we provide an example of how to use CuPBoP to execute a CUDA program.
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```bash
cd examples/vecadd
# Compile CUDA source code (both host and kernel) to bitcode files
clang++ -std=c++11 vecadd.cu \
-I../.. --cuda-path=$CUDA_PATH \
--cuda-gpu-arch=sm_50 -L$CUDA_PATH/lib64 \
-lcudart_static -ldl -lrt -pthread -save-temps -v || true
# Apply compilation transformations on the kernel bitcode file
$CuPBoP_PATH/build/compilation/kernelTranslator \
vecadd-cuda-nvptx64-nvidia-cuda-sm_50.bc kernel.bc
# Apply compilation transformations on the host bitcode file
$CuPBoP_PATH/build/compilation/hostTranslator \
vecadd-host-x86_64-unknown-linux-gnu.bc host.bc
# Generate object files
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llc --relocation-model=pic --filetype=obj kernel.bc
llc --relocation-model=pic --filetype=obj host.bc
# Link with runtime libraries and generate the executable file
g++ -o vecadd -fPIC -no-pie \
-I$CuPBoP_PATH/runtime/threadPool/include \
-L$CuPBoP_PATH/build/runtime \
-L$CuPBoP_PATH/build/runtime/threadPool \
host.o kernel.o \
-I../.. -lc -lx86Runtime -lthreadPool -lpthread
# Execute
./vecadd
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```
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## How to contribute?
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Any kinds of contributions are welcome.
Please refer to [Contribution.md](./CONTRIBUTING.md) for more detail.
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## Related publications
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If you want to refer CuPBoP in your projects, please cite the related
papers:
- [COX: Exposing CUDA Warp-Level Functions to CPUs](https://dl.acm.org/doi/abs/10.1145/3554736)
- [CuPBoP: CUDA for Parallelized and Broad-range Processors](https://arxiv.org/abs/2206.07896)
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## Contributors
- [Ruobing Han](https://drcut.github.io/)
- Jun Chen
- Bhanu Garg
- Xule Zhou
- John Lu
- [Chihyo Ahn](https://upcp.ece.gatech.edu/2021/09/01/chihyo-ahn/)
- Haotian Sheng
- Blaise Tine
- [Hyesoon Kim](https://faculty.cc.gatech.edu/~hyesoon/)
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## Acknowledgements
- [POCL](http://portablecl.org/) is an open-source
OpenCL implementations that based on LLVM.
We reuse some code from it
(e.g., apply optimizations, load/store LLVM IRs).
- [Hetero-Mark](https://github.com/NUCAR-DEV/Hetero-Mark)
and [Rodinia Benchmark](https://github.com/yuhc/gpu-rodinia)
are two benchmark suites
for heterogeneous system computation.
CuPBoP uses them as integrated test to verify the correctness.
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- [moodycamel::ConcurrentQueue](<https://github.com/cameron314/concurrentqueue/tree/master>)
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is a fast multi-producer,
multi-consumer lock-free concurrent queue for C++11.
CuPBoP uses it as the task queue for launching and executing kernels.