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Session 4: An introduction to GPU-based computing

Leads: Brian Stucky (SCINet Computational Biologist), Heather Savoy (SCINet Computational Biologist)


This session will introduce key concepts of GPU-based computing, including how they differ from CPUs and what kinds of computing tasks can benefit from GPUs. There will also be a hands-on tutorial showing how to use the GPUs on the Atlas cluster and how to evaluate the effect of using GPUs on your computation time. A primary goal of this session is to help participants build intuition about when GPUs might be useful in scientific computing and how to use them.


Session objectives

The goals of this session are to:

  • Understand the key differences between CPUs and GPUs.
  • Build intuition about when GPUs can be helpful in scientific computing.
  • Provide a practical introduction to using GPUs for scientific computing with Python.


Agenda

This session will begin with a short presentation followed by an interactive tutorial.

  • Presentation: An introduction to GPU-based computing
  • Python tutorial:
    • GPU-based computing with CuPy
      • Creating and working with GPU-based multidimensional arrays
      • Accelerated computing with 1D CuPy arrays (vectors)
      • Accelerated computing with 2D CuPy arrays (matrices)
    • GPU-based computing with RAPIDS
      • GPU-based dataframes with RAPIDS
      • GPU-based machine learning with RAPIDS


Tutorial instructions

Steps to prepare for the tutorial:

  1. Login to Atlas Open OnDemand at https://atlas-ood.hpc.msstate.edu/. For more information on SCINet login procedures, see the SCINet access user guide.

  2. Launch a Jupyter-A100 session. Under the Interactive Apps menu, select Jupyter-A100. Specify the following input values on the page:

    • Python Version: 3.10.8
    • Lab or Notebook: JupyterLab
    • Account Name: geospatialworkshop
    • Partition Name: gpu-a100
    • QOS: ood – Max Time: 8-00:00:00
    • Number of hours: 4
    • Number of nodes: 1
    • Number of tasks: 1
    • Additional Slurm Parameters: --gres=gpu:a100_1g.10gb:1 --mem=32G --reservation=workshop

    Click Launch. The screen will update to the Interactive Sessions page. When your Jupyter session is ready, the top card will update from Queued to Running and a Connect to Jupyter button will appear. Click Connect to Jupyter.

  3. Open a terminal session within JupyterLab. Within JupyterLab, open the “File” menu, then “New” -> “Terminal”.

  4. Copy the Session 4 material from the workshop project space to your temporary workshop folder.

     mkdir -p /90daydata/shared/$USER/session_4-gpu_computing/
 cd /90daydata/shared/$USER/session_4-gpu_computing/
 cp /project/geospatialworkshop/session_4-gpu_computing/gpu_computing_python.ipynb .

    If you DID NOT participate in Session 2 or 3, please also follow these steps:

    Create a symbolic link to your temporary workshop folder from your home directory. You will then have a shortcut called my_geoworkshop in your home directory that points to your workshop folder. This shortcut will allow you to access your workshop files from JupyterLab:

     ln -s /90daydata/shared/$USER ~/my_geoworkshop

    If you participated in Session 2 or 3, you do not need to do anything else except for follow along during the tutorial session! If you did not participate in Session 2 or 3, you will need to complete the additional steps below.

  5. Setup kernel for JupyterLab. In the workshop project space, there is a workshop_venv virtual environment for the packages we will be using during the workshop tutorials. You will create a kernel called grwg_workshop to access from JupyterLab.

    To create a new kernelspec from the virtual environment:

     source /project/geospatialworkshop/workshop_venv/bin/activate
 ipython kernel install --name "grwg_workshop" --user
 cp /project/geospatialworkshop/grwg_workshop.json ~/.local/share/jupyter/kernels/grwg_workshop/kernel.json

  6. Restart JupyterLab. You will need to restart JupyterLab in order to use the new kernel you created for step 5, above. Follow these steps:

    1. Close the JupyterLab tab in your browser.
    2. Return to the Open OnDemand tab in your browser, and click the Delete button that is inside the card for the running “Jupyter-A100” session. (If you do not see the running session cards in Open OnDemand, click the interactive sessions icon next to “Interactive Apps” at the top of the page.) Wait a few seconds for the page to refresh.
    3. Repeat the instructions for step 2, above, to start a new JupyterLab session. Open OnDemand should automatically reuse the settings you entered the first time you launched JupyterLab.

  7. Start session and select kernel: Once you are in JupyterLab, navigate to ~/my_geoworkshop/session_4-gpu_computing in the left navigation pane, and open the gpu_computing_python.ipynb notebook by double-clicking that file. Then, select your kernel by opening the “Kernel” menu then “Change kernel…”. A pop-up will appear with a dropdown menu containing the grwg_workshop kernel we made above. Click on the grwg_workshop kernel and click the Select button.

  8. Follow along during the tutorial session!