Introduction to GPU Computing

What is a GPU and how can it be used for scientific computing? What GPU resources does Princeton provide? This workshop will answer these questions and have participants run code written in Python, R, Julia and MATLAB on the GPU nodes of the Research Computing clusters. Participants will also gain hands-on experience with getting started with CUDA, a low-level GPU programming model. This workshop presumes no previous knowledge of GPU computing.

Learning objectives: Attendees will learn about GPU hardware and come away with the ability to write and execute simple, compiled programs that use GPUs.

Knowledge prerequisites: Basic Linux and some exposure to a compiled programming language (e.g., C/C++, Fortran, Java).

Hardware/software prerequisites: (1) Bring a laptop which can connect to the eduroam wireless network. You will also need to be able to Duo authenticate to use campus resources. (2) Have an SSH client installed on your laptop. (3) Register for an account on Adroit. This is the cluster we will use for demonstration purposes. Make sure you can SSH to Adroit before the workshop.

Workshop format: Lecture and hands-on

Instructor bio: Jonathan Halverson is the Research Software and Computing Training Lead with PICSciE and Research Computing. He has an expertise in data science and he is a founding organizer of the TensorFlow & PyTorch User Group at Princeton. Prior to his current position, Jonathan performed polymer physics research at the Max Planck Institute for Polymer Research and nanoscience research at Brookhaven National Laboratory. He holds a Ph.D. in Chemical Engineering from CUNY.

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Co-hosted with: GradFUTURES

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