Computer Science Thesis Oral
— 5:00pm
Location:
In Person
-
Newell-Simon Hall 3002
Speaker:
BYUNGSOO JEON
,
Ph.D. Candidate, Computer Science Department, Carnegie Mellon University
https://madfunmaker.github.io/
Automated and Portable Machine Learning System
The explosive popularity of Machine Learning (ML) drives the rapid advance in ML models, systems, and hardware. Nevertheless, it remains challenging and labor-intensive to quickly adapt existing ML systems to new models and hardware to maximize performance. We observe that it is attributed to existing ML systems falling short in portability and automatability across several crucial layers of a system stack. However, building a portable ML system requires non-trivial modeling of intricate commonalities and differences of diverse ML models or platforms. In addition, automating ML system layers introduces the challenge of designing practical search space and search algorithms to customize optimizations to a given model and hardware.
In this thesis, we aim to tackle the challenges above of building an automated and portable ML system with a focus on crucial ML system layers. Specifically, the thesis explores ways to build an efficient system that automates 1) integration of ML backends and 2) ML parallelisms and makes them more portable. We develop a user interface and system stack to be more portable across different backends and underlying hardware. We also design practical search space and algorithms to automate backend placement and parallelism.
Thesis Committee:
Tianqi Chen (Co-chair)
Zhihao Jia (Co-chair)
Gregory R. Ganger
Luis Ceze (University of Washington)