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The VLDB Journal
Erschienen in: The VLDB Journal 6/2016

01.12.2016 | Regular Paper

SkyAlign: a portable, work-efficient skyline algorithm for multicore and GPU architectures

verfasst von: Kenneth S. Bøgh, Sean Chester, Ira Assent

Erschienen in: The VLDB Journal | Ausgabe 6/2016

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Abstract

The skyline operator determines points in a multidimensional dataset that offer some optimal trade-off. State-of-the-art CPU skyline algorithms exploit quad-tree partitioning with complex branching to minimise the number of point-to-point comparisons. Branch-phobic GPU skyline algorithms rely on compute throughput rather than partitioning, but fail to match the performance of sequential algorithms. In this paper, we introduce a new skyline algorithm, SkyAlign, that is designed for the GPU, and a GPU-friendly, grid-based tree structure upon which the algorithm relies. The search tree allows us to dramatically reduce the amount of work done by the GPU algorithm by avoiding most point-to-point comparisons at the cost of some compute throughput. This trade-off allows SkyAlign to achieve orders of magnitude faster performance than its predecessors. Moreover, a NUMA-oblivious port of SkyAlign outperforms native multicore state of the art on challenging workloads by an increasing margin as more cores and sockets are utilised.
Vorheriger Artikel Diverse and proportional size-l object summaries using pairwise relevance
Nächster Artikel ADS: the adaptive data series index

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Fußnoten
1
Without loss of generality and to simplify exposition, we assume smaller values are better, but to handle mixed preferences (e.g. Table 1) is a straightforward adaptation.
 
2
Obtained by counting low-level operations in Algorithm 1 of GGS [2] (the branch-free dominance test). Branching DTs are ill-suited to GPUs and have unpredictable, variable cost.
 
3
Hyper-threading also hides latencies, but it is not as impactful as the features that we will explicitly analyse.
 
4
We assume “large” memory to simplify the algorithm.
 
5
Manhattan norm is the sum of all attribute values.
 
6
Either via PCIe transfer from CPU (host) memory or because the previous GPU operator in the query plan completes.
 
7
Progressive skyline algorithms [17] can output solution points as they are discovered, in contrast to an algorithm that must fully complete before any solution point can be confirmed.
 
8
Strictly speaking, some warps run concurrently while others queue, and the order in which they are queued is unpredictable.
 
10
This is the number of concurrent threads on our GPU.
 
11
CUDA 7 and C++ are similar enough that converting from the former to the latter is trivial.
 
13
BSkyTree does not use the pre-filter that the other three methods use, but its pivot selection routine has a similar effect.
 
14
Up to eight instructions can be retired in a cycle if they are ready for execution, but the front-end (the instruction fetch-decode cycle) populates the queue at a slower rate.
 
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Metadaten
Titel
SkyAlign: a portable, work-efficient skyline algorithm for multicore and GPU architectures
verfasst von
Kenneth S. Bøgh
Sean Chester
Ira Assent
Publikationsdatum
01.12.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
The VLDB Journal / Ausgabe 6/2016
Print ISSN: 1066-8888
Elektronische ISSN: 0949-877X
DOI
https://doi.org/10.1007/s00778-016-0438-1

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