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Title: Template Skycube Algorithms for Heterogeneous Parallelism on Multicore and GPU Architectures

Citation Type: Conference Paper

Publication Year: 2017

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Abstract: Multicore CPUs and cheap co-processors such as GPUs create opportunities for vastly accelerating database queries. However, given the differences in their threading models, expected granularities of parallelism, and memory subsystems, effectively utilising all cores with all co-processors for an intensive query is very difficult. This paper introduces a novel templating methodology to create portable, yet architecture-aware, algorithms. We apply this methodology on the very compute-intensive task of calculating the *skycube*, a materialisation of exponentially many skyline query results, which finds applications in data exploration and multi-criteria decision making. We define three parallel templates, two that leverage insights from previous skycube research and a third that exploits a novel point-based paradigm to expose more data parallelism. An experimental study shows that, relative to the state-of-the-art that does not parallelise well due to its memory and cache requirements, our algorithms provide an order of magnitude improvement on either architecture and proportionately improve as more GPUs are added.

Url: https://dl.acm.org/citation.cfm?id=3035962

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Authors: Bøgh, Kenneth, S; Šidlauskas, Darius; Assent, Ira; Chester, Sean

Conference Name: SIGMOD '17 Proceedings of the 2017 ACM International Conference on Management of Data

Publisher Location: Chicago, IL

Data Collections: IPUMS USA

Topics: Methodology and Data Collection, Other

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