Research

  • ML for Systems and Systems for ML
    • MLops optimization and use of ML to improve system performance
  • Video Analytics
    • Efficient computer vision model inference
  • Cloud & Distributed Systems
    • Serverless, distributed protocols, and systems on disaggregate data centers
  • Databases
    • Learned indexing and concurrency control
  • Parallel and Supercomputing
    • CXL, GPGPU, OpenMP, lock-free data structures, many-core synchronization
  • Data Science
    • We also develop scalable and efficient software for physicists at CERN’s ALTAS and for bioinformatician

The followings are some of my ongoing projects.

Everest
Recently, the impressive accuracy of deep neural networks (DNNs) has created great demands on practical analytics over video data. Although accurate, practical video analytics systems require high efficiency.  In this project, we investigate various techniques to speed up video analytics on various platforms and hardware.
  • Status: Ongoing
  • Areas: Video analytics, MLSys
  • Keywords: Deep Learning Systems, Video Analytics
  • Selected Publications:
    • PengFei Zhang, C. Han, Eric Lo: “More is Less – Byte-quantized models are faster than bit-quantized models on the edge”.  In Proceedings of BigData, 2022.
    • PengFei Zhang, Eric Lo, Baotong Lu: “High-Performance Depthwise and Pointwise Convolutions on Mobile Devices”.  In Proceedings of AAAI, 2020.
    • Ziliang Lai, Chenxia Han, Chris Liu, PengFei Zhang, Eric Lo, Ben Kao: “Top-K Deep Video Analytics: a Probabilistic Approach“.  SIGMOD, 2021.

Architectural Conscious Data Processing

Continued to thrive for ever-faster processing is leading computer scientists to directly leverage modern hardware innovations in the design of software systems. This trend is further amplified by the collapse of improvements in linear chip clock frequency scaling due to physical limits.  Therefore, software system designs that disregard hardware innovations are doomed to failure.

Our goal is to study different approaches to leveraging modern/emerging hardware to accelerate data processing in big data management.  Hardware under consideration includes many-core processors (e.g., GPGPU), data-parallel processing unit (SIMD), CXL, etc.

  • Status: Ongoing
  • Areas: Database × Operating Systems × Supercomputing
  • Keywords: Persistent memory, SIMD, RDMA, many-core, GPGPU, software-hardware co-design
  • Selected Publications:
    • Chaichon. Wongkham, B. Lu, C. Liu, Z. Zhong, Eric Lo, T. Wang: “Are Updatable Learned Indexes Ready?“. In Proceedings of VLDB, 2022.
    • B. Lu, J. Ding, Eric Lo, U.Farooq Minhas, T. Wang: “APEX: A High-Performance Learned Index on Persistent Memory“. In Proceedings of VLDB, 2022.
    • B. Lu, X. Hao, T. Wang, Eric Lo: “Dash: Scalable Hashing on Persistent Memory“.  In Proceedings of VLDB, 2020. (ACM SIGMOD Research Highlight Award 2021)
    • W. Xu, Eric Lo, P. Zhang: “DIFusion: Fast Skip-Scan with Zero Space Overhead“.  In Proceedings of IEEE ICDE Conference, 2018.
    • W. Xu, Z. Feng, Eric Lo: “Fast Multi-column Sorting in Main-Memory Column-Stores“.  In Proceedings of ACM SIGMOD Conference, 2016.
    • Z. Feng, Eric Lo, B. Kao, W. Xu: “ByteSlice: Pushing the Envelop of Main Memory Query Processing with a New Storage Layout“.  In Proceedings of ACM SIGMOD Conference, 2016.
    • Z. Feng, Eric Lo: “Accelerating aggregation using intra-cycle parallelism“.  In Proceedings of IEEE ICDE Conference, 2015.