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Introduction to GPU Computing
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Details
What is a GPU? How can it be used for scientific computing? What GPU resources does Princeton provide? This workshop will answer these questions and give participants the opportunity to run sample code written in Python, R, Julia and MATLAB on the GPU nodes of one of the Research Computing clusters. Participants will also gain hands-on experience with getting started with CUDA, a low-level GPU programming model and platform. This workshop presumes no previous knowledge of GPU computing.
Learning objectives: Attendees will learn about GPU hardware and come away with the ability to run GPU codes.
Knowledge prerequisites: Basic Linux is required; knowledge of a compiled programming language (e.g., C/C++, Fortran, Java) would be beneficial.
Hardware/software prerequisites: You will need a laptop and an account on the Adroit cluster (https://researchcomputing.princeton.edu/adroit).
Workshop format: Lecture and hands-on
Room info: https://classroominfo.princeton.edu/View.aspx?bl_id=0585&fl_id=01&rm_id=0585_01_M16&bc=BENDC&img=0585103.JPG&rn=103
Speakers
Jonathan Halverson
Research Software and Computing Training Lead
Princeton University
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.
Hosted By
Co-hosted with: GradFUTURES
Contact the organizers