Conveners
Session 6: Parallel Training Methods
- Sam Foreman (LCF)
- Alessandro Lovato (PHY)
Description
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.
