Work-efficient parallel skyline computation for the GPU

Abstract

The skyline operator returns records in a dataset that provide optimal trade-offs of multiple dimensions. State-of-theart 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.

MEMOCODE 2015 design contest: Continuous skyline computation

“…Although the submissions to this year's contest focused solely on CPU-based implementation, a number of recent works have explored computing the skyline on GPU and FPGA. For example, [14] shows how the dominance operator can be computed in a branch-free manner to make it suitable for GPU and [15] details a partitioning scheme providing further improvements. Meanwhile, [16] presents a highly scalable parallel FPGA technique for computing the skyline; the authors later generalized this idea into a new computational structure called a shifter list [17].…”

Computing Influence of a Product through Uncertain Reverse Skyline

“…Parallelizing Reverse Skyline Queries. Though there exist many works on parallelizing the standard skyline queries ( [9], [18], [1], [22], [3], [27] for survey), there are only few works devoted to parallelizing the reverse skyline queries. Park et al [21] propose an approach for parallelizing both dynamic and reverse skyline queries in MapReduce by inventing a novel quad-tree based data indexing.…”

Template Skycube Algorithms for Heterogeneous Parallelism on Multicore and GPU Architectures

“…Following [2,8,21,23,42,43] and for the sake of point-based partitioning (described in Section 2.2), we also denote with a bitmask the per-dimension relationship between two points. Bit i of Bp⊕q is set iff p[i]⊕q [i].…”

“…Point-based partitioning Recent shared-memory skyline algorithms [2,8,21,23,42,43] avoid explicit point-topoint comparisons using transitivity with respect to a common "pivot" point. Appendix B.2 reviews this.…”