Intro to AI Series: Parallel Training Methods for AI
From October 1 - November 12, 2024, the ALCF will host a 7-part weekly virtual training series to teach undergraduates and graduates the fundamentals of using world-class supercomputers to advance the use of AI for research.
Intro to AI Series: Session 6
We present modern parallelism techniques and discuss how they can be used to train and distribute large models across many GPUs.
Lecturer
Sam Foreman is a Computational Scientist with a background in high energy physics, currently working as a postdoc in the ALCF. He is generally interested in the application of machine learning to computational problems in physics, particularly within the context of high-performance computing. Sam's current research focuses on using deep generative modeling to help build better sampling algorithms for simulations in lattice gauge theory.
AI for Science Speaker
Arvind Ramanathan is a computational biologist in the Data Science and Learning Division at Argonne National Laboratory and a senior scientist at the University of Chicago Consortium for Advanced Science and Engineering (CASE). His research interests are at the intersection of data science, high performance computing and biological/biomedical sciences. He will be speaking about Autononous Discovery for Biological Systems Design.