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Speaker: Prof. Manya Ghobadi, MIT

Title: Optimizing AI Systems with Optical Technologies

Abstract: Our society is rapidly becoming reliant on deep neural networks (DNNs). New datasets and models are invented frequently, increasing the memory and computational requirements for training. The explosive growth has created an urgent demand for efficient distributed DNN training systems. In this talk, I will discuss the challenges and opportunities for building next-generation DNN training clusters. In particular, I will propose optical network interconnects as a key enabler for building high-bandwidth ML training clusters with strong scaling properties. Our design enables accelerating the training time of popular DNN models using reconfigurable topologies by partitioning the training job across GPUs with hybrid data and model parallelism while ensuring the communication pattern can be supported efficiently on an optical interconnect. Our results show that compared to similar-cost interconnects, we can improve the training iteration time by up to 5x.

Bio: Manya Ghobadi is an assistant professor at the EECS department at MIT. Before MIT, she was a researcher at Microsoft Research and a software engineer at Google Platforms. Manya is a computer systems researcher with a networking focus and has worked on a broad set of topics, including data center networking, optical networks, transport protocols, and network measurement. Her work has won the best dataset award and best paper award at the ACM Internet Measurement Conference (IMC) as well as Google research excellent paper award.