Liang Geng

Liang Geng

Ph.D. Student

The Ohio State University

Biography

Hi, I’m Liang Geng (耿亮), a third-year Ph.D. student in the Computer Science Department, OSU. I’m interested in computer systems, especially GPU and RDMA programming. My current research is repurposing NVIDIA RT Cores to accelerate non-graphics workloads, such as geospatial data processing. My advisors are Prof. Xiaodong Zhang and Dr. Rubao Lee. Before joining the OSU, I was a senior engineer at Alibaba DAMO Academy supervised by Dr. Wenyuan Yu, where I developed GPU support for libgrape-lite and was the initial contributor to GrapeScope and Vineyard. I was fortunately advised by Prof. Yanfeng Zhang and Dr. Hao Wang while pursuing my Master’s Degree. They helped me to start my research journey.

Interests
  • Geospatial Data Processing
  • Parallel Computing
  • Distributed Systems
Education
  • Ph.D., Computer Science, 2022 - Current

    The Ohio State University, USA

  • M.Eng., Computer Science, 2016 - 2019

    Northeastern University, China

  • B.Eng., Software Engineering, 2012 - 2016

    Liaoning Technical University, China

Recent Publications

(2024). RayJoin: Fast and Accurate Spatial Join with Ray Tracing. In ICS.

PDF Code Slides

(2024). RR-Compound: RDMA-fused gRPC for Low Latency and High Throughput with an Easy Interface. In TPDS.

Code

(2024). Ingress: an automated incremental graph processing system. In VLDB Journal.

(2023). Efficient Multi-GPU Graph Processing with Remote Work Stealing. In ICDE.

Code

(2022). Linking Entities across Relations and Graphs. In ICDE.

Code

(2022). An RDMA-enabled In-memory Computing Platform for R-tree on Clusters. In TSAS.

PDF

(2021). Automating Incremental Graph Processing with Flexible Memoization. In VLDB.

PDF

(2020). Automating Incremental and Asynchronous Evaluation for Recursive Aggregate Data Processing. In SIGMOD.

PDF

(2019). Catfish: Adaptive RDMA-enabled R-Tree for Low Latency and High Throughput. In ICDCS.

PDF

(2019). HYPHA: a framework based on separation of parallelisms to accelerate persistent homology matrix reduction. In ICS.

PDF

(2019). SEP-graph: finding shortest execution paths for graph processing under a hybrid framework on GPU. In PPoPP.

PDF Code Slides