This research project examines the design and deployment of specialized processors to improve the throughput and energy efficiency of large-scale data processing. In particular, our focus is on relational database query processing and the design of a database processing unit or DPU. Like GPUs, DPUs are domain-specific processors which can support a range of database oriented computations. Our first DPU design focuses on analytic queries of large data sets.
|L. Wu, R. J. Barker, M. A. Kim, K. A. Ross. Hardware Partitioning for Big Data Analytics. In IEEE Micro: Top Picks from Computer Architecture Conferences (Top Picks), 34(3): pages 109 -- 119, May/June 2014.|
|L. Wu, A. Lottarini, T. Paine, M. A. Kim, K. A. Ross. Q100: The Architecture and Design of a Database Processing Unit. In the International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), March 2014.|
|L. Wu, R. J. Barker, M. A. Kim, K. A. Ross. Navigating Big Data with High-Throughput, Energy-Efficient Data Partitioning. In the International Symposium on Computer Architecture (ISCA), pages 249 -- 260, June 2013. Top Picks in Computer Architecture Selection.|
This material is based upon work supported by the National Science Foundation under Grant No. CCF-1065338. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.