67 lines
1.7 KiB
Markdown
67 lines
1.7 KiB
Markdown
# CuPBoP: Cuda for Parallelized and Broad-range Processors
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## Introduction
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CuPBoP (Cuda for parallelized and broad-range processors) is a framework
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aims to execute CUDA source code on non-NVIDIA devices,
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including CPU, GPU and other architectures.
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## Install
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### Prerequisites
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* Linux: Verified on Ubuntu 18.04
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* LLVM10.0
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* NVIDIA CUDA-toolkit
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* x86 CPU
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* pthread
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* GCC 7.5.0
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### Installation
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1. Clone from github
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```bash
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git clone https://github.com/cupbop/CuPBoP
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cd CuPBoP
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```
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2. Build the transformer for NVVM IR to LLVM IR for X86
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```bash
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mkdir build && cd build
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cmake .. -DLLVM_CONFIG_PATH=`which llvm-config` # need path to llvm-config
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make
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```
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## Run Vecadd samples
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```bash
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# Generate bitcode from human-readable LLVM IR
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llvm-as ../compilation/examples/vecadd/kernel-cuda-nvptx64-nvidia-cuda-sm_61.ll
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# use LLVM passes to transform NVVM IR (SPMD) to LLVM IR (MPMD+SIMD).
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# NOTE: we hard-code the grid size (1, 1, 1)
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# and block size (1024, 1, 1) into the generated LLVM IR
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./compilation/nvvm2x86 \
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../compilation/examples/vecadd/kernel-cuda-nvptx64-nvidia-cuda-sm_61.bc \
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kernel.bc 1 1 1 32 1 1
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# generate object file from LLVM IR
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llc --filetype=obj kernel.bc
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# link generated kernel function
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# with host function and generate excutable file
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g++ ../compilation/examples/vecadd/host.cpp \
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kernel.o -lpthread -o vecadd_example
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# execute the executable file
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./vecadd_example
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```
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## Contribution
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We sincerely appreciate all kinds of contributions.
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Please refer to [CONTRIBUTING](docs/CONTRIBUTING.md) for the contributing guideline.
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## Author
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* [Ruobing Han](https://drcut.github.io/)
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* [Hyesoon Kim](https://www.cc.gatech.edu/~hyesoon/)
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