CSE Faculty Candidate Seminar - Yuke Wang

Name: Yuke Wang, Ph.D. student at University of California, Santa Barbara

Date: Tuesday, February 6, 2024 at 11:00 am

Location: Coda Building, Second Floor, Room 230 (Google Maps link)

Link: The recording of this in-person seminar will be uploaded to CSE's MediaSpace

Title: Intelligent Software in the Era of Deep Learning

Abstract: With the end of Moore's Law and the rise of compute- and data-intensive deep-learning (DL) applications, the focus on arduous new processor design has shifted towards a more effective and agile approach -- Intelligent Software to maximize the performance gains of DL hardware like GPUs.

In this talk, I will first highlight the importance of software innovation to bridge the gap between the increasingly diverse DL applications and the existing powerful DL hardware platforms. The second part of my talk will recap my research work on DL system software innovation, focusing on bridging the 1) Precision Mismatch between DL applications and high-performance GPU units like Tensor Cores (PPoPP '21 and SC '21), and 2) Computing Pattern Mismatch between the sparse and irregular DL applications such as Graph Neural Networks and the dense and regular tailored GPU computing paradigm (OSDI '21 and OSDI '23). Finally, I will conclude this talk with my vision and future work for building efficient, scalable, and secure DL systems.

Bio: Yuke Wang is a final-year Doctor of Philosophy (Ph.D.) candidate in the Department of computer science at the University of California, Santa Barbara (UCSB). He got his Bachelor of Engineering (B.E.) in software engineering from the University of Electronic Science and Technology of China (UESTC) in 2018. At UCSB, Yuke is working with Prof. Yufei Ding (Now at UC at San Diego, CSE). Yuke's research interests include Systems & Compiler for Deep Learning and GPU-based High-performance Computing. His projects cover graph neural network (GNN) optimization and its acceleration on GPUs. Yuke’s research has resulted in 20+ publications (with 10 first-authored papers) in top-tier conferences, including OSDI, ASPLOS, ISCA, USENIX ATC, PPoPP, and SC. Yuke’s research outcome has been adopted for further research in industries (e.g., NVIDIA, OctoML, and Alibaba) and academia (e.g., University of Washington and Pacific Northwest National Laboratory). Yuke is also the recipient of the NVIDIA Graduate Fellowship 2022 (Top-10 out of global applicants) and has industry experience at Microsoft Research, NVIDIA Research, and Alibaba. The ultimate goal of Yuke’s research is to facilitate efficient, scalable, and secure deep learning in the future.