We present modern parallelism techniques and discuss how they can be used to train and distribute large models across many GPUs.

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.

Alessandro Lovato's research is in theoretical nuclear physics and focuses on understanding properties of atomic nuclei and neutron-star matter in terms of the individual interactions among the constituent protons and neutrons. Nuclei exhibit fascinating, self-emerging, quantum-mechanical properties, which are experimentally probed by several facilities worldwide, including the Argonne Tandem Linac Accelerator System. Precisely modeling their structure and electroweak interactions is also relevant to access neutrino properties and to test the fundamental symmetries of the Standard Model. The same nuclear forces that determine the structure of nuclei are responsible for the stability of neutron stars against gravitational collapse and are imprinted in the gravitational wave signal. His talk will cover Variational Learning for Quantum Wave Functions.