Title: Enhance Database Query Performance through Software Optimization and Hardware Adaptation
Date: 2/19 (Monday)
Time: 11 AM - 12:30 PM
Location: Klaus 3100
Virtual: Team Link
teams.microsoft.com
Jiashen Cao
Ph.D. Computer Science
School of Computer Science
Georgia Institute of Technology
Committee:
Dr. Joy Arulraj (Co-Advisor) - School of Computer Science, Georgia Institute of Technology
Dr. Hyesoon Kim (Co-Advisor) - School of Computer Science, Georgia Institute of Technology
Dr. Divya Mahajan - School of Electrical and Computer Engineering and Computer Science, Georgia Institute of Technology
Dr. Kexin Rong - School of Computer Science, Georgia Institute of Technology
Abstract:
Database systems have been incorporating machine learning algorithms to provide rich information. Complex machine learning algorithms become the new performance bottleneck for database systems, with video database systems particularly suffering from this performance degradation. In this talk, I will discuss my endeavors to enhance query performance through software optimizations and hardware adaptations.
I first present FiGO, an approach to utilize the just-enough-accurate algorithm to process data. Following that, I will introduce Hydro, which employs the concept of adaptive query processing. This strategy allows for executing queries with optimal planning and resource allocation. I will also cover my research efforts in analyzing and optimizing GPU database systems. Lastly, I will present my proposed research idea of leveraging existing deep learning compilers to construct an optimal query plan for machine learning queries.