Minhui Xie
Ph.D. Student
Department of Computer Science and Technology
Room 8-201, East Main Building, Tsinghua University, Beijing, China
Email: xmh19 AT mails dot tsinghua dot edu dot cn
About Me
I am Minhui Xie, a third-year Ph.D. student from Tsinghua University. I am a system researcher. My research focus is building efficient systems for at-scale machine learning, with emerging hardware (e.g., persistent memory, modern GPUs). I am so excited about the interact field between ML and System.
Education
Ph.D.
Department of Computer Science, Tsinghua University
2019 - present
B.S.
Department of Computer Science, Nanjing University
GPA 4.84/5.00
Rank 1st /160 (core courses), 3rd /160 (all courses)
2015 - 2019
Publication Lists
Projects
Fleche - efficient GPU-resident embedding cache
2021-2021
- In this work, we identify the DRAM bandwidth scarcity problem and propose Fleche to address it. Fleche’s key idea is absorbing hot accesses via a lightweight GPU-resident embedding cache.
- Fleche gets up to 4.0x speedup of end-to-end inference throughput over NVIDIA HugeCTR, a well-known highly optimized industrial system.
- It is published in EuroSys’22.
NVMRec - embedding server with byte-addressable non-volatile memory
2019-2020
- NVMRec is the first system that applies byte-addressable NVM technology for recommendation system in practice. It solves the essential problems caused by poor matching between NVM hardware features and recommendation workloads.
- It has been deployed at Kuaishou’s datacenters and successfully withstood the access pressure of over 26 billion video recommendations every day. It got 30% cost-savings while maintaining service performance.
- It has been reported by the industry and official media such as Tsinghua(link), Kuaishou, Intel(link), and People.cn(link).
Kraken - memory efficient continual learning for at-scale recommendation systems
2019-2020
- Kraken redesigns the age-old structure of embedding tables for continual learning and tailors the optimizer algorithm to make thrift use of DRAM. It can trisect the memory usage while keeping model performance.
- It has been cited and highly rated by companies including Facebook, Tencent, Alibaba, ByteDance, Kuaishou, and Huawei. It was also incorporated into a popular open-source book on GitHub in the area of MLSys, OpenMLSys(link).
- It is published in SC’20.
Grants & Awards
Awards During Ph.D.
- Tsinghua First-class Scholarship
2021
- Student Grant from USENIX FAST
2021
Selected awards before Ph.D.
- Outstanding Graduate of Nanjing University
2019
- Tung OOCL Scholarship (5%)
2018
- National Scholarship (2%)
2017
- National Second Prize, China Undergraduate Mathematical Contest in Modeling
2017
- Meritorious Winner, MCM/ICM
2017
- Tung OOCL Scholarship (5%)
2016
- Excellent student at Nanjing University (5%)
2016
Services
- SIGCOMM 2022, Artifact reviewer
- USENIX ATC 2022, Artifact reviewer
- OSDI 2022, Artifact reviewer
- IEEE Transactions on Parallel and Distributed Systems (TPDS), 2022, Reviewer
- EuroSys 2022, Artifact reviewer
- Long-term volunteer of ChinaSys
Invited Talks
- Fleche - efficient GPU-resident embedding cache
- NVIDIA, Beijing, China - May 26, 2022
- EuroSys’22, Rennes, France - Apr 04, 2022
- Kraken - memory efficient continual learning for at-scale recommendation systems
- Huawei, Beijing, China - Mar 25, 2022
- Tsinghua, Beijing, China - Nov 19, 2020
- SC’20, San Diego, US - Nov 11, 2020
Teaching
- TA, Computer Organization and Architecture, Tsinghua University, Spring 2022
- TA, Computer Organization and Architecture, Tsinghua University, Spring 2021
- TA, Computer Organization and Architecture, Tsinghua University, Spring 2020
- TA, Introduction to Computer System, Nanjing University, Fall 2017