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Solid State Drives for Big Data and Little Clients

  • Author(s): Li, Jing;
  • Advisor(s): Swanson, Steven;
  • Papakonstantinou, Yannis
  • et al.

Big data analytics open challenges for efficiently processing, moving and storing data. Existing research works focus on the algorithm design or applying hardware accelerators. However, in current systems,data transfer (from secondary storage or remote nodes) becomes an increasingly important but less-optimized performance bottleneck.

This thesis first presents HippogriffDB, a data-warehouse system that delivers efficient, scalable analytical performance with GPU and SSD. HippogriffDB achieves high efficiency by reconciling the bandwidth mismatch between GPU and IO with improved data transfer mechanism and data compression strategies. Experiment results demonstrate that HippogriffDB outperforms state-of-the-art GPU-based databases by up to 10×.

The thesis then presents SoftFlash. SoftFlash offloads some essential database functionalities from modern data warehouse applications into storage devices. By using hardware accelerators, on-chip processors, as well as software approaches, SoftFlash manages to reduce the data traffic over network and to improve the query execution time.

This thesis also covers experimental studies on the energy prospective of flash memory in the context of mobile computing. While the flash hardware is well known to be energy and power efficient, the software stack consumes significantly higher amount of power and energy.

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