Run cutlass with gpgpu-sim

1. Download and install cuda 9.1

      wget https://developer.nvidia.com/compute/cuda/9.1/Prod/local_installers/cuda_9.1.85_387.26_linux
   
      sudo sh cuda_9.1.85_387.26_linux
   

   Set up the bashrc file, adding lines to the file

       export PATH=/usr/local/cuda-9.1/bin:$PATH
       export CUDA_INSTALL_PATH=/usr/local/cuda-9.1
       export LD_LIBRARY_PATH=$CUDA_INSTALL_PATH/lib64:$LD_LIBRARY_PATH 
   

2. Download the gpgpusim 4.0

       sudo apt-get install build-essential xutils-dev bison zlib1g-dev flex libglu1-mesa-dev
   
       sudo apt-get install doxygen graphviz
   
       sudo apt-get install python-pmw python-ply python-numpy libpng-dev python-matplotlib
   
       sudo apt-get install libxi-dev libxmu-dev freeglut3-dev
   
       git clone https://github.com/gpgpu-sim/gpgpu-sim_distribution.git
   
     Make and build GPGPUSIM:
   
       make clean
   
       source setup_environment
   
       make
   

3. Download the gpgpusim version cutlass

       git clone https://github.com/accel-sim/gpu-app-collection.git
   

4. Run and build the cutlass benchmark

       cd gpu-app-collection/src/cuda/cutlass-bench/
   
       git submodule update --init —recursive
   
       mkdir build && cd build
   
       cmake .. -DUSE_GPGPUSIM=1 -DCUTLASS_NVCC_ARCHS=70 && make cutlass_perf_test
   
       cd tools/test/perf && ln -s ../../../../binary.sh . && ./binary.sh
   

   (Then copy the configs, i.e. cp -a ~/gpgpu-sim_distribution/configs/tested-cfgs/SM7_TITANV/* . )

       ./cutlass_perf_test --m=1760 --n=16 --k=1760 --kernels=wmma_gemm_nn --iterations=1 —providers=cutlass
   

   Reference and more information at:

   https://github.com/accel-sim/gpu-app-collection/tree/release/src/cuda/cutlass-bench