headshot of Leana, wearing a black turtle neck and black rimmed glasses, smiling

Speaker: Leana Golubchik

Date: Wednesday, November 30th, 2022

Time: 3:30 - 4:30 pm

Location: HFH 1132

Host: Amr El Abbadi

Title: Predicting Performance of Distributed ML Systems


Deep learning has made substantial strides in many applications. New training techniques, larger datasets, increased computing power, and easy-to-use machine learning frameworks all contribute to this success. An important
missing piece is that deep learning frameworks do not assist users with provisioning cloud resources; most users need to try different job configurations to determine the resulting training performance. When resources are shared among hundreds of jobs, this approach quickly becomes infeasible. In this talk, we will focus on our approach to predicting performance metrics and scheduling algorithms that use these metrics to guide resource allocation. Our goal is to broaden the population of users capable of developing deep learning models and applying them to novel applications.


Leana Golubchik is the Stephen and Etta Varra Professor of Computer Science (with a joint appointment in Electrical and Computer Engineering) at USC. She also serves as the Director of the Women in Science and Engineering (WiSE)
program. Prior to that, she was on the faculty at the University of Maryland and Columbia University. Leana received her PhD from UCLA. Her research interests are broadly in the design and evaluation of large scale distributed systems, including hybrid clouds and data centers and their applications in data analytics, machine learning, and privacy. Leana is the Editor-in-Chief of the ACM Transactions on Modeling and Performance Evaluation of Computing Systems
(ToMPECS). She received several awards, including the IBM Faculty Award, the NSF CAREER Award, and the Okawa Foundation Award; she is a member of the IFIP WG 7.3 and a Fellow of AAAS.