Schedule

Time	Section
9:00 AM	Opening Remarks
9:05 AM	Special Speaker: William Dally NVIDIA
9:55 AM	Break
10:00 AM	Keynote Speaker: Chris Lattner Modular.ai
10:55 AM	Coffee Break
11:10 AM	VMR2L: Virtual Machines Rescheduling Using Reinforcement Learning in Data Centers
11:20 AM	ZeRO++: Extremely Efficient Collective Communication for Large Model Training
11:30AM	Poster Session 1
12:30AM	Lunch
1:30 PM	Invited Speaker: Atlas Wang UT Austin
2:10 PM	Tea Break
2:30 PM	On the Promise and Challenges of Foundation Models for Learning-based Cloud Systems Management
2:40 PM	Predicting User Experience on Laptops from Hardware Specifications
2:50 PM	Poster Session 2
3:50 PM	Tea Break
4:00 PM	Competition Track Highlights
4:15 PM	Competition Winner Talks
4:50 PM	Competition Q&A

Speakers

Chris Lattner

Keynote Speaker

Programming Languages Challenges in Large Scale Machine Learning

Chris Lattner is the co-founder and CEO of Modular AI. He cofounded the LLVM Compiler infrastructure, the Clang compiler, the Swift programming language, the MLIR compiler infrastructure, the CIRCT project (applying MLIR to hardware design), and have contributed to many other commercial and open source projects at Apple, Tesla, Google, and SiFive. Previously, Chris led the Engineering and Product teams at SiFive, the Google TensorFlow team, the Tesla Autopilot team, and worked for Apple managing the Developer Tools department.

William Dally

Dr. Bill Dally brings extensive expertise in circuit design, high performance computing, and machine learning. Dally is the SVP of Research at NVIDIA, which he joined as a chief scientist in 2009. Previously, Dally led research teams as a professor at Stanford for 12 years and MIT for 11 years. He co-founded two companies. He is a member of the National Academy of Engineering, a Fellow of the American Academy of Arts & Sciences, a Fellow of the IEEE and the ACM, and has received the ACM Eckert-Mauchly Award, the IEEE Seymour Cray Award, and the ACM Maurice Wilkes award. He has published over 250 papers, holds over 120 issued patents, and is an author of four textbooks.

Atlas Wang

Efficient Generative Inference with Heavy Hitters and Beyond

Professor Zhangyang "Atlas" Wang is an associate professor at UT Austin, where he holds the Temple Foundation Endowed Faculty Fellowship #7, in the Chandra Family Department of Electrical and Computer Engineering. He is also a faculty member of UT Computer Science (GSC) and the Oden Institute CSEM program. Wang directs AI Research and Technology at Picsart part time. Wang is a recipient of numerous awards, including NSF CAREER, IEEE AI's 10 To Watch, and Google Research Scholar awards.