research-article

Work-efficient parallel skyline computation for the GPU

Editors: Chen Li, Volker Markl Authors:

Kenneth S. Bøgh,

Ira AssentAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 8, Issue 9

Pages 962 - 973

https://doi.org/10.14778/2777598.2777605

Published: 01 May 2015 Publication History

Abstract

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-efficiency and respectable throughput, rather than maximal throughput, to achieve orders of magnitude faster performance.

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Cited By

Kilari EKailasam S(2025)Distributed Computation of Skyline Probability over Uncertain PreferencesProceedings of the 26th International Conference on Distributed Computing and Networking10.1145/3700838.3700859(125-133)Online publication date: 4-Jan-2025
https://dl.acm.org/doi/10.1145/3700838.3700859
Martin Nevot M(2025)Emerging skycubeKnowledge and Information Systems10.1007/s10115-024-02320-2Online publication date: 13-Jan-2025
https://doi.org/10.1007/s10115-024-02320-2
Li CGu YQi JYu GAl Hasan MXiong L(2022)Parallel Skyline Processing Using Space Pruning on GPUProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557414(1074-1083)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557414
Show More Cited By

Index Terms

Work-efficient parallel skyline computation for the GPU
1. Computing methodologies
  1. Computer graphics
    1. Graphics systems and interfaces
      1. Graphics processors
2. Information systems
  1. Data management systems
    1. Database management system engines

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Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 8, Issue 9

May 2015

76 pages

ISSN:2150-8097

Editors:
Chen Li
University of California, Irvine
,
Volker Markl
TU Berlin

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VLDB Endowment

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Published: 01 May 2015

Published in PVLDB Volume 8, Issue 9

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Cited By

Kilari EKailasam S(2025)Distributed Computation of Skyline Probability over Uncertain PreferencesProceedings of the 26th International Conference on Distributed Computing and Networking10.1145/3700838.3700859(125-133)Online publication date: 4-Jan-2025
https://dl.acm.org/doi/10.1145/3700838.3700859
Martin Nevot M(2025)Emerging skycubeKnowledge and Information Systems10.1007/s10115-024-02320-2Online publication date: 13-Jan-2025
https://doi.org/10.1007/s10115-024-02320-2
Li CGu YQi JYu GAl Hasan MXiong L(2022)Parallel Skyline Processing Using Space Pruning on GPUProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557414(1074-1083)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557414
Chen JZhang KRen TWu ZGao H(2022)GAM: A GPU-Accelerated Algorithm for MaxRS Queries in Road NetworksJournal of Computer Science and Technology10.1007/s11390-022-2330-337:5(1005-1025)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s11390-022-2330-3
Wang WZhang JSun MKu W(2019)A scalable spatial skyline evaluation system utilizing parallel independent region groupsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-018-0519-428:1(73-98)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s00778-018-0519-4
Endres MRudenko L(2018)A Tour of Lattice-Based Skyline AlgorithmsHandbook of Research on Investigations in Artificial Life Research and Development10.4018/978-1-5225-5396-0.ch006(96-122)Online publication date: 2018
https://doi.org/10.4018/978-1-5225-5396-0.ch006
Zois VGupta DTsotras VNajjar WRoy JEvripidou SStenström PO'Boyle M(2018)Massively parallel skyline computation for processing-in-memory architecturesProceedings of the 27th International Conference on Parallel Architectures and Compilation Techniques10.1145/3243176.3243187(1-12)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1145/3243176.3243187
Koizumi KEades PHiraki KInaba M(2018)BJR-tree: fast skyline computation algorithm using dominance relation-based tree structureInternational Journal of Data Science and Analytics10.1007/s41060-018-0098-x7:1(17-34)Online publication date: 31-Jan-2018
https://doi.org/10.1007/s41060-018-0098-x
Kulkarni RMomin B(2018)Skyline Computation for Big DataData Science and Big Data Analytics10.1007/978-981-10-7641-1_23(267-276)Online publication date: 2-Aug-2018
https://doi.org/10.1007/978-981-10-7641-1_23
Liu JLi XRen KSong J(2018) Parallelizing uncertain skyline computation against n ‐of‐ N data streaming model Concurrency and Computation: Practice and Experience10.1002/cpe.484831:4Online publication date: 6-Nov-2018
https://doi.org/10.1002/cpe.4848
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