You can choose either of two ways below.
sudo apt-get install -y wget build-essential xutils-dev bison zlib1g-dev flex \ libglu1-mesa-dev git g++ libssl-dev libxml2-dev libboost-all-dev git g++ \ libxml2-dev vim python-setuptools python-dev build-essential python-pip pip3 install pyyaml plotly psutil wget http://developer.download.nvidia.com/compute/cuda/11.0.1/local_installers/cuda_11.0.1_450.36.06_linux.run sh cuda_11.0.1_450.36.06_linux.run --silent --toolkit rm cuda_11.0.1_450.36.06_linux.run
You may wonder why we(I) recommend using Docker. Imagine that you need to install many apps and each app rely on different envs (for example, A => gcc8 and B => gcc10). You will soon find that it’s very complicated/impossible to build an env that all apps are compatible with each other. Then you will think of Docker in which each env is independent from each other.
To get docker image of ACCEL-SIM env
docker pull accelsim/ubuntu-18.04_cuda-11
To get ACCEL-SIM
git clone -b dev https://github.com/accel-sim/accel-sim-framework.git
How to launch the container with ACCEL-SIM? Try to figure out by reading LABI
To build ACCEL-SIM
# in Docker, <CUDA_DIR>=/usr/local/cuda-11.0 export CUDA_INSTALL_PATH=<CUDA_DIR> export PATH=$CUDA_INSTALL_PATH/bin:$PATH pip3 install -r requirements.txt # in docker we can skip source ./gpu-simulator/setup_environment.sh make -j -C ./gpu-simulator/
To test your-built ACCEL-SIM
If you have problems about ACCEL-SIM, reference its webpage first.