Doctoral Thesis Proposal - Hongyi Jin
November 9, 2026 12:30PM—2:00PM
Location:
9115
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Gates and Hillman Centers
Speaker:
HONGYI JIN,
Ph.D. Student, Computer Science Department, Carnegie Mellon University
https://www.linkedin.com/in/hongyi-jin-97b943250
LLMs have become a major GPU workload, and a moving one: serving strategies, batch shapes and expert routing, operators, and GPU architectures all keep changing. Today's stacks turn each change into a re-engineering effort because their interfaces are too rigid to program against.
This thesis argues that adaptation should become programming, with an abstraction designed for whoever programs it. LLM microserving gives developers composable sub-request APIs for orchestrating serving; Event Tensor gives the compiler a dependency IR for scheduling dynamic megakernels; and TIRx Harness gives AI agents not a higher-level language but a deliberately minimal, hardware-close IR with verifiable feedback, on which they write kernels competitive with, and often faster than, expert references. As proposed work, I will extend this design to a new GPU architecture, such as NVIDIA Rubin, where the analyses must model new hardware primitives and agents must learn to exploit new hardware features.
Thesis Committee
Tianqi Chen (Co-Chair)
Todd C. Mowry (Co-Chair)
Zhihao Jia
Gennady Pekhimenko (University of Toronto)
Contact
Matt Stewart