Jinkun Geng (耿金坤)

Senior Postdoctoral Associate@SUNY

I am a Senior Postdoctoral Associate in Stony Brook University. I got my PhD degree (2019.9-2024.1) from Department of Computer Science, Stanford University. I have broad interest in networking and systems. Currently, I am focusing on building high-performance and fault-tolerant distributed systems by leveraging the advanced techniques including synchronized clocks and trustworthy execution environment (TEE). My CV is attached here .

//intro

Education

MSc Computer Science

2016 - 2019
I worked with Professor Dan Li on high-performance networking systems.

PhD Computer Science

2019 - 2024
I worked with Professor Balaji Prabhakar, Professor Anirudh Sivaraman and Professor Mendel Rosenblum on high-performance and fault-tolerant distributed systems, with a specific focus on synchronized clocks.

Postdoc Computer Science

2025 - Present
I am working with Professor Shuai Mu and Professor Anirudh Sivaraman on fault-tolerant distributed systems and system verification.

Research Projects

Nezha: Deployable and High-Performance Consensus Using Synchronized Clocks

PhD@Stanford
2021.03 - 2023.09
  • Develop a common primitive based on synchronized clocks, deadline-ordered multicast (DOM)
  • Develop a protocol based on DOM, which is called Nezha. Nezha has proved to outperform the typical consensus protocols, including Multi-Paxos, Raft, NOPaxos, Fast Paxos, EPaxos, etc.
  • Nezha is open-sourced here, and we plan to integrated Nezha with some industrial systems which require high-performance consensus.

CloudEx: Fair-Access Financial Trading System in Cloud

PhD@Stanford
2019.08 - 2023.06
  • Implement fair trading mechanism in CloudEx, and provide user-friendly APIs
  • Performance optimization for CloudEx
  • Implement fault tolerance for CloudEx

Rima: An RDMA-Accelerated Model-Parallelized Solution to Large-Scale Matrix Factorization

Master@THU
2017.12 - 2018.06
  • Re-design the architecture of distributed matrix factorization. Leverage ring-based architecture instead of PS-based architecture to eliminate the centralized bottleneck and data redundancy
  • Design one-step transformation strategy to halve the communication workload for large-scale matrix factorization
  • Design three partial randomness strategies to add more robustness to the algorithm
  • Overlap the disk I/O overheads with the communication/computation overheads
  • Conduct a comparative testbed experiment between Rima and DSGD

HiPS: Hierarchical Parameter Synchronization in Large-Scale Data Center Network

Master@THU
2017.5 - 2018.03
  • Incorporate data center network (DCN) topology with distributed machine learning (DML) to boost the performance
  • Design high-efficient synchronization algorithms on top of server-centric topologies to better embrace the benefit of RDMA
  • Implement a prototype of BCube+HiPS in Tensorflow, and conduct comparative experiments with a 2-layer BCube testbed

LOS: High Performance and Strong Compatible User-level Network Stack

Master@THU
2015.10 - 2017.05
  • Design and implement a user-level stack based on DPDK, to achieve high performance and strong compatibility
  • Implement user-level Netfilter with dynamic library
  • Port Nginx on LOS without changing the source code

Publications

  • Jinkun Geng, Anirudh Sivaraman, Balaji Prabhakar, Mendel Rosenblum. Nezha: Deployable and High-Performance Consensus Using Synchronized Clocks (Preprint, Repository)
    49th International Conference on Very Large Data Bases (VLDB 2023)
  • Ahmad Ghalayini, Jinkun Geng, Vighnesh Sachidananda, Vinay Sriram, Yilong Geng, Balaji Prabhakar, Mendel Rosenblum, Anirudh Sivaraman. CloudEx: A Fair-Access Financial Exchange in the Cloud (PDF)
    18th Workshop on Hot Topics in Operating Systems (HotOS 2021)
  • Jinkun Geng, Dan Li, Shuai Wang. Fela: Incorporating Flexible Parallelism and Elastic Tuning to Accelerate Large-Scale DML (Preprint, PPT)
    36th IEEE International Conference on Data Engineering (ICDE 2020)
  • Jinkun Geng, Dan Li, Shuai Wang. Rima: An RDMA-Accelerated Model-Parallelized Solution to Large-Scale Matrix Factorization (PDF, PPT, Poster)
    35th IEEE International Conference on Data Engineering (ICDE 2019)
  • Shuai Wang, Dan Li, Jinkun Geng. Geryon: Accelerating Distributed CNN Training by Network-Level Flow Scheduling (Preprint)
    IEEE International Conference on Computer Communications(INFOCOM 2020)
  • Jinkun Geng, Dan Li, Yang Cheng, Shuai Wang, Junfeng Li. HiPS: Hierarchical Parameter Synchronization in Large-Scale Distributed Machine Learning (PDF, PPT)
    ACM SIGCOMM 2018 Workshop on Network Meets AI & ML (NetAI 2018)
  • Yukai Huang, Jinkun Geng, Du Lin, Bin Wang, Junfeng Li, Ruilin Ling, Dan Li. LOS: A High Performance and Compatible User-level Network Operating System (PDF, PPT)
    1st Asia-Pacific Workshop on Networking (APNet 2017)
  • Talks

  • Nezha: A High-Performance Consensus Protocol Using Accurately Synchronized Clocks
    Stanford Platform Lab Winter Review, 2022
  • CloudEx: Building a Jitter-free Financial Exchange in the Cloud
    Stanford Platform Lab Seminar, 2020
  • Fela: Incorporating Flexible Parallelism and Elastic Tuning to Accelerate Large-Scale DML
    IEEE International Conference on Data Engineering (ICDE 2020)
  • Accelerating Distributed Machine Learning by Smart Parameter Server
    3rd Asia-Pacific Workshop on Networking (APNet 2019)
  • Rima: An RDMA-Accelerated Model-Parallelized Solution to Large-Scale Matrix Factorization
    35th IEEE International Conference on Data Engineering (ICDE 2019)
  • HiPS: Hierarchical Parameter Synchronization in Large-Scale Distributed Machine Learning
    ACM SIGCOMM 2018 Workshop on Network Meets AI & ML (NetAI 2018)
  • LOS: A High Performance and Compatible User-level Network Operating System
    1st Asia-Pacific Workshop on Networking (APNet 2017)
  • Work Experiences

    Clockwork

    Palo Alto
    Senior Software Researcher
    2024.01 – 2025.05
    • Develop advanced flow scheduling and load balancing algorithms.
    • Learned a lot of non-technical stuff.

    Clockwork

    Palo Alto
    Software Engineer
    2022.06 – 2022.09
    • Refactor the codebase of Nezha and prepare for open-sourcing.
    • Test and evaluate Nezha.
    • Explore the application of clock synchronization in new areas (concurrentcy control).

    Google

    Mountain View (Virtual)
    Software Engineer
    2020.06 – 2020.09
    Mentor: Hui Su
    • Optimize video encoding performance
    • Implement Warped transformation for motion vector predictio
    • Implement DBSCAN algorithm for clustering motion vectors.

    Rewards

  • Outstanding Master Graduate in Tsinghua (3 out of 141) @THU (2019.07)
  • Outstanding Master Dissertation in Tsinghua (7 out of 141) (PDF) @THU (2019.06)
  • HPDC Student Travel Grant@HPDC Committee (2019) (2019.06)
  • ICDE Student Travel Awards@ICDE Committee (2019) (2019.03)
  • Samsung Enterprise Scholarship@Samsung Group (2014.10)