Scalable parallelization of skyline computation for multi-core processors

Chester, Sean; Sidlauskas, Darius; Assent, Ira; Bøgh, Kenneth S.

doi:10.1109/icde.2015.7113358

Cited by 33 publications

(49 citation statements)

References 20 publications

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“…Unlike previous work (e.g. [4,12]), which only counts DTs, work recognizes that, while MTs are much cheaper that DTs, they are also significantly more frequent.…”

Section: Skyline Computationmentioning

confidence: 82%

“…Even for multi-core [4], designing work-efficient, parallel skyline algorithms is non-trivial. State-of-the-art sequential skyline algorithms [12,20] derive performance from extensive use of trees, recursion, strict ordering of computation, and unpredictable branching.…”

Section: Hotelmentioning

confidence: 99%

“…Our non-traditional trade-off, then, is throughput: we do not maximize throughput, but we are work-efficientrunning an order of magnitude faster than state-of-the-art multicore [4]-while remaining bound by the availability of id x0 x1 x2 Dominated? Pruned?…”

Section: Hotelmentioning

confidence: 99%

“…The Hybrid multi-core algorithm [4] is a point-based method that flattens the tree into an array structure for better access patterns and processes points in blocks of size α to improve parallelism. We describe this class in more detail in section 4.1.…”

Section: Partition-based Skyline Algorithmsmentioning

confidence: 99%

“…MTs have been shown to drastically improve performance by reducing DTs [4,12,20]. In this section, we briefly review the recursive, point-based approach that introduced MTs in literature, including its limitations (Section 4.1).…”

Section: Gpu-friendly Partitioningmentioning

confidence: 99%

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Work-efficient parallel skyline computation for the GPU

2015

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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.

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“…Unlike previous work (e.g. [4,12]), which only counts DTs, work recognizes that, while MTs are much cheaper that DTs, they are also significantly more frequent.…”

Section: Skyline Computationmentioning

confidence: 82%

Section: Hotelmentioning

confidence: 99%

Section: Hotelmentioning

confidence: 99%

Section: Partition-based Skyline Algorithmsmentioning

confidence: 99%

Section: Gpu-friendly Partitioningmentioning

confidence: 99%

See 3 more Smart Citations

Work-efficient parallel skyline computation for the GPU

2015

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Parallelization of group‐based skyline computation for multi‐core processors

Zhu

et al. 2017

Concurrency and Computation

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Summary Skyline computation is particularly useful in multi‐criteria decision‐making applications. However, it is inadequate to answer queries that need to analyze not only individual points but also groups of points. Compared to the traditional skyline computation, computing group‐based skyline is much more complicated and expensive. This computational challenge promotes us to use modern computing platforms to accelerate the computation. In this paper, we introduce a novel multi‐core algorithm to compute group‐based skyline. We first compute the skyline layers of a data set in parallel, which are a critical intermediate result. In the algorithm, we maintain an efficiently updatable data structure for the shared global skyline layers, which is used to minimize dominance tests and maintain high throughput. Then we design an efficient parallel algorithm to find group‐based skyline based on the skyline layers. Extensive experimental results on real and synthetic data sets show that our algorithms achieve 10‐fold speedup with 16 parallel threads over state‐of‐the‐art sequential algorithms on challenging workloads.

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Parallelization of skyline probability computation over uncertain preferences

Zhu

et al. 2017

Concurrency and Computation

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Summary Query processing over uncertain preferences is very common in real‐life situations, because many times, we cannot model users' preferences as strict partial orders. In this paper, we investigate skyline queries over uncertain preferences. The latest state‐of‐the‐art algorithm, called Usky‐base algorithm, makes significant advances. However, it still needs to be perfected in 2 aspects. (1) Theoretic analysis: The correctness of the algorithm is not fully verified. (2) Efficiency: Due to the heavy calculation introduced by adopting inclusion‐exclusion principle to express the skyline probability, it needs massive time when computing skyline probabilities for large data sets. To address the above 2 concerns, we first review the Usky‐base algorithm and lemmas it based on. Then we propose a novel parallel algorithm, called Parallel‐sky, to compute skyline probability of a given object. Moreover, we propose an adding algorithm and a deleting algorithm to deal with dynamic scenarios where new objects are added in and outdated objects are deleted out. Furthermore, we extend our algorithm from computing skyline probability of a given object to all objects in a data set. We conduct extensive experiments on real and synthetic data sets to validate the effectiveness and efficiency of our proposals.

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Scalable parallelization of skyline computation for multi-core processors

Cited by 33 publications

References 20 publications

Work-efficient parallel skyline computation for the GPU

Work-efficient parallel skyline computation for the GPU

Parallelization of group‐based skyline computation for multi‐core processors

Parallelization of skyline probability computation over uncertain preferences

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