Venkatesh Babu Radhakrishnan (IISc)

Dima Damen (University of Bristol)

Efstratios Gavves (University of Amsterdam)

Stefanie Jegelka (MIT EECS and TU Munich)

Alex Kolesnikov (OpenAI)

Presented by Peter Gehler and Matthias Kümmerer

The tutorial will provide a hands-on introduction to the fundamental principles and practical
techniques of high-performance AI computing, including distributed training strategies, GPU
and multi-node acceleration, memory-efficient model optimization, and inference
acceleration. It will cover best practices for leveraging HPC environments, including
supercomputing clusters and cloud-based AI infrastructures, to scale deep learning
workloads efficiently. Participants will learn how to implement state- of-the-art techniques
such as model parallelism, mixed-precision training, and advanced scheduling policies to
maximize performance and reduce energy consumption.