The NVIDIA A100 GPU will soon be found across the majority of the Research Computing clusters. This powerful accelerator offers a theoretical performance of 9.7 TFLOPS in double precision and 19.5 in single. Specialized hardware units on the GPUs called Tensor Cores allow for even faster speeds. In order to take full advantage of the A100, most applications require users to modify their input scripts.

This workshop provides an overview of the features of the A100 GPU along with specific use cases for deep learning (PyTorch and TensorFlow) and HPC. Tools for performance profiling and for measuring data transfer rates will be presented.

If you encounter any difficulties with the material in this guide then please send an email to cses@princeton.edu or attend a help session.

This guide was created by Jonathan Halverson and members of Princeton Research Computing.