gpgpusim

The image is created from nvidia/cuda:11.0.3-cudnn8-devel-ubuntu18.04

software information

• Ubuntu 18.04

• cuda 11.0.3

• cudnn 8

• gpgpusim

  • link: https://github.com/gpgpu-sim/gpgpu-sim_distribution
  • commit: 90ec3399763d7c8512cfe7dc193473086c38ca38
  • location: /opt/gpgpu-sim_distribution

Usage

1. pull the image to the local machine

   $ docker pull ghcr.io/a1245967/gpgpusim
   $ docker tag ghcr.io/a1245967/gpgpusim gpgpusim

   You can check the image information by the command docker images after you pull the image.

2. create a container to run gpgpusim

   The following command is the simplest way to create the container.

   $ docker run -it --name container_name gpgpusim bash

3. put the cuda example into the container

   You can put the example code at the folder where you mount the container. For example, you can use git to pull your code at $HOME path.

   $ cd /root
   $ git clone https://github.com/A1245967/gpgpusim.git

4. compile the cuda code by nvcc

   $ cd cuda-example/
   $ nvcc --cudart shared -o vecadd vecadd.cu -gencode arch=compute_75,code=compute_75

   Note: You need to add flag --cudart shared if you use the gpgpusim as the simulator.
   The number 75 refers to the SM version. You would need to set it based on the GPGPU-Sim config -gpgpu-ptx-force-max-capability you use.

5. move the execution file to the config folder and run the program

   $ mv vecadd ../gpu-configs/SM75_RTX2060
   $ cd ../gpu-configs/SM75_RTX2060
   $ ./vecadd