Keynotes
Keynote speakers
Title: Distributed Deep Learning: Progress and Challenges
Abstract: I am going to present the progress and challenges of multinode distributed deep learning. Even though GPUs are continuously gaining more computation throughput, it is still very time-consuming to train state-of-the-art deep neural network models. For better scalability and productivity, it is paramount to accelerate the training process by using multiple GPUs. I will first introduce the basics of distributed deep learning. Second, I will explain the current distributed deep learning framework implementations. Finally, the future directions of distributed deep learning are discussed.
Bio: Takuya Akiba is VP of ML Systems at Preferred Networks, Inc., working on research and development for making deep learning faster and more scalable. He received a Ph.D. in information science and technology from the University of Tokyo, Japan, in 2015.
Title: Shared Clusters for Machine Learning: Through the looking glass
Abstract: With recent advances in machine learning, large enterprises incorporate machine learning models across a number of products. To facilitate training of these models, enterprises use shared, multi-tenant cluster of machines equipped with accelerators like GPUs. Similar to data analytics clusters, operators aim to achieve high resource utilization while providing resource isolation and fair sharing across users. In this talk we will first present characterization of machine learning workloads from a multi-tenant GPU cluster at Microsoft. We then present how various aspects of these workloads such as gang scheduling and locality constraints affect resource utilization and efficiency. Based on this analysis we discuss new research to improve efficiency, utilization both for individual jobs and across the cluster.
Bio: Shivaram Venkataraman is an Assistant Professor in the Computer Science 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.