Work-Efficient Parallel Skyline Computation for the GPU

Research output: Research - peer-reviewJournal article

The skyline operator returns records in a dataset that provide optimal trade-offs of multiple dimensions. State-of-the-art skyline computation involves complex tree traversals, data-ordering, and conditional branching to minimize the number of point-to-point comparisons. Meanwhile, GPGPU computing offers the potential for parallelizing skyline computation across thousands of cores. However, attempts to port skyline algorithms to the GPU have prioritized throughput and failed to outperform sequential algorithms.

In this paper, we introduce a new skyline algorithm, designed for the GPU, that uses a global, static partitioning scheme. With the partitioning, we can permit controlled branching to exploit transitive relationships and avoid most point-to-point comparisons. The result is a non-traditional GPU algorithm, SkyAlign, that prioritizes work-effciency and respectable throughput, rather than maximal throughput, to achieve orders of magnitude faster performance.
Original languageEnglish
JournalProceedings of the VLDB Endowment
Volume8
Issue number9
Pages (from-to)962-973
Number of pages12
ISSN2150-8097
DOIs
StatePublished - May 2015
EventInternational Conference on Very Large Databases - Kohala Coast, Hawaii, United States
Duration: 31 Aug 20155 Sep 2015
Conference number: 41

Conference

ConferenceInternational Conference on Very Large Databases
Number41
CountryUnited States
CityKohala Coast, Hawaii
Period31/08/201505/09/2015

    Research areas

  • GPU, skyline, work-efficiency, Databases, Query processing

See relations at Aarhus University Citationformats

Activities

ID: 86499964