Reproducibility Submission of pap223 & apdx190

Experiment Workflow

1 Prepare Hardware & Software Environments

You need computers or servers with Ubuntu 20.04, CUDA 11.3, Nsight System, and V100/RTX2080Ti/A100/RTX3090 GPUs.

If local GPUs are not available, you can rent GPU servers used in this work:

• login to https://en.gpushare.com/, navigate to console -> Instance and Data -> My instance -> Create instance
• select the GPU you want
• select Instance image -> Official image -> PyTorch 1.12.0 -> Python 3.8 -> Cuda 11.3
• then you can access the GPU server through ssh.

CUDA 11.3 is already installed on the GPU servers, but you still need to install nsight system:

• download the nsight system installation package from https://developer.nvidia.com/downloads/assets/tools/secure/nsight-systems/2023_2/nsightsystems-linux-cli-public-2023.2.1.122-3259852.deb/ to your local computer
• if the above link is not valid (version updated), you can click download on https://developer.nvidia.com/nsight-systems/get-started to download the installation package (Linux CLI only .deb Installer) to your local computer
• upload the nsight system installation package through scp to the GPU servers
• install the dependent library: sudo apt-get install libglib2.0-0:amd64
• install nsight system: sudo dpkg -i NsightSystems-linux-cli-public-{installation-package-version-number}.deb

2 Execute the code

• get the code from https://github.com/nicolaswilde/thread-block-scheduling
• upload the code to the GPU servers
• execute:
  • make GPGPU=V100 on V100
  • make GPGPU=RTX2080TI on RTX2080Ti
  • make GPGPU=A100 on A100
  • make GPGPU=RTX3090 on RTX3090

3 Check the Experiment Results

After execution, some information is printed on the terminal. You will find the profiling files in the directory result. You can download the profiling files to your local computer (if you are using GPU servers) and open the profiling files with a host version (with GUI) of nsight system.

Following the workflow above, the authors provide the experiment results on V100, RTX2080Ti, A100, and RTX3090 at https://github.com/nicolaswilde/thread-block-scheduling/tree/main/my-result. You can check the log files and the nsys-rep files.