Systems and ML: Opportunities and Challenges for Symbiotic Research
Shivaram Venkataraman (University of Wisconsin-Madison)
Abstract: The intersection of systems and ML has led to the development of two main research areas: the development of efficient systems for ML and the optimization of existing systems software using ML. In this we talk I discuss opportunities and challenges that span these two areas. First I will present the design of scheduling frameworks used in ML clusters and show how knowing more about ML workloads can significantly improve cluster efficiency. Next I will show how ML optimizers can similarly benefit from richer knowledge about systems software. Finally, I will conclude by discussing opportunities for symbiotic research across the two areas.
Bio: Shivaram Venkataraman is an Assistant Professor in the Computer Sciences Department at University of Wisconsin-Madison. His research interests are in designing systems and algorithms for large scale data analysis and machine learning. Before coming to Madison, he was a post-doctoral researcher in the Systems Research Group at Microsoft Research in Redmond. Previously, he completed his PhD from UC Berkeley where he was advised by Ion Stoica and Mike Franklin.