Build ACCEL-SIM

You can choose either of two ways below.

Setup on Ubuntu 18.04

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

Using Docker

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

. travis.sh

If you have problems about ACCEL-SIM, reference its webpage first.