Doctoral Speaking Skills Talk - Hongyi Jin
June 8, 2026 4:00PM—5:00PM
Location:
6501
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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/
Modern GPU workloads, especially large language model (LLM) inference, suffer from kernel launch overheads and coarse synchronization that limit inter-kernel parallelism. Recent megakernel techniques fuse multiple operators into a single persistent kernel to eliminate launch gaps and expose inter-kernel parallelism, but struggle to handle dynamic shapes and data-dependent computation in real workloads. We present Event Tensor, a unified compiler abstraction for dynamic megakernels.
Event Tensor encodes dependencies between tiled tasks, and enables first-class support for both shape and data-dependent dynamism. Built atop this abstraction, our Event Tensor Compiler (ETC) applies static and dynamic scheduling transformations to generate high-performance persistent kernels. Evaluations show that ETC achieves state-of-the-art LLM serving latency while significantly reducing system warmup overhead.
Contact
Matt Stewart