Table of Contents
Xingda Wei (魏星达)
Institute of Parallel And Distributed Systems
School of Software
Shanghai Jiao Tong University
Software Building, 800 Dongchuan Rd., Shanghai, China
Zip/Postal Code: 200240
Email: wxdwfc at sjtu dot edu dot cn (email@example.com)
I am an assistant professor in the institute of parallel and distributed systems (IPADS) at Shanghai Jiaotong University (SJTU).
My research focuses on improving the performance and reliability for computer systems and distributed systems, particularly how to design systems with emergency hardware technologies and design systems with machine learning technologies.
I am looking for self-motivated students interested in building distributed systems with modern hardware technolgies. Drop me an E-mail if you want to join us!
My research focuses on building fast and reliable distributed systems. I primarily think about the following question: how can we leverage the heterogeneous hardware features in modern datacenters and powerful machine learning technologies to build the next-generation distributed systems? Specifically, I focus on the following topics:
- How to co-design systems with modern datacenter networking? Modern datacenter fast interconnects like RDMA and SmartNIC offer extremely powerful in-networking computing capabilities. How can we fully utilize the power of these networking features? My related projects include:
- DST(Decentralized Scalar Timestamp): Scalable timestamps for efficient MVCC [NSDI'21]
- Machine learning for systems. The advances in machine learning (ML) technologies also bring huge optimization space for computer systems. Besides providing powerful heuristics, the ML methods can change the complexity of traditional algorithms (e.g., sorting and indexing). My related projects include:
- Systems with storage class memory. Storage class memory, aka, NVM has finally come to the public market. Besides high performance, its memory-like interfaces give us opportunities to re-think the design of traditional storage systems. My related projects include:
- Fast remote persistent memory: Characterizing and Optimizing Remote Persistent Memory with RDMA and NVM [USENIX ATC'21]
- Next-generation cloud computing infrastructure. Serverless computing is a promising future direction for future cloud computing; it has the benefits of auto-scaling and pay-as-you-go, just to name a few. However, current systems have huge performance costs when supporting the above features. How can we build the next-generation serverless system? [MITOSIS OSDI'23]
Awards and Honors
- 华为火花奖, 2022
- ACM 中国优秀博士学位论文提名奖 (ACM China Doctoral Dissertation Award), 2021
- ACM ChinaSys 优秀博士学位论文奖 (ACM ChinaSys Doctoral Dissertation Award), 2021
- Honorable Mention of the ACM SIGOPS Dennis M. Ritchie Award, 2021 Link
- Huawei OlympusMons Pioneer Award, 2020 Link
- Microsoft Research Asia Fellowship Award, 2018 Link
- Excellent Bachelor Thesis, 2015
- [OSDI] No Provisioned Concurrency: Fast RDMA-codesigned Remote Fork for Serverless Computing. Xingda Wei, Fangming Lu, Tianxia Wang, Jinyu Gu, Yuhan Yang, Rong Chen, and Haibo Chen. 17th USENIX Symposium on Operating Systems Design and Implementation, Boston, MA, US, July 2023. Pre-print
- [USENIX ATC] KRCORE: a microsecond-scale RDMA control plane for elastic computing. Xingda Wei, Fangming Lu, Rong Chen, and Haibo Chen. 2022 USENIX Annual Technical Conference, Carlsbad, CA, US. July 2022. [paper] | AE | github
Awarded with USENIX Badges: Artifacts Available, Artifacts Functional, Results Reproduced.
- [USENIX ATC] Xingda Wei, Xiating Xie, Rong Chen, Haibo Chen, Binyu Zang. Characterizing and Optimizing Remote Persistent Memory with RDMA and NVM. 2021 USENIX Annual Technical Conference, July 2021. [paper]
- [USENIX ATC] Xingda Wei, Sijie Shen, Rong Chen, and Haibo Chen. Replication-driven Live Reconfiguration for Fast Distributed Transaction Processing. 2017 USENIX Annual Technical Conference, Santa Clara, CA, US, July 2017. [paper]
- [TOCS] Haibo Chen, Rong Chen, Xingda Wei, Jiaxin Shi, Yanzhe Chen, Zhaoguo Wang, Binyu Zang, Haibing Guan. Fast In-memory Transaction Processing using RDMA and HTM. ACM Transactions on Computer Systems, Vol. 35, No. 1, Article 3, July, 2017. [paper]
- [SOSP] Xingda Wei, Jiaxin Shi, Yanzhe Chen, Rong Chen, and Haibo Chen. Fast In-memory Transaction Processing using RDMA and HTM. 25th ACM Symposium on Operating Systems Principles, Monterey, CA, USA, October 2015. [paper] [slides] [poster] ACM DL github
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Full version paper: ACM TOCS
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