A GPU-friendly Geometric Data Model and Algebra for Spatial Queries

Harish Doraiswamy, Juliana Freire

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The availability of low cost sensors has led to an unprecedented growth in the volume of spatial data. Unfortunately, the time required to evaluate even simple spatial queries over large data sets greatly hampers our ability to interactively explore these data sets and extract actionable insights. While Graphics Processing Units∼(GPUs) are increasingly being used to speed up spatial queries, existing solutions have two important drawbacks: they are often tightly coupled to the specific query types they target, making it hard to adapt them for other queries; and since their design is based on CPU-based approaches, it can be difficult to effectively utilize all the benefits provided by the GPU. As a first step towards making GPU spatial query processing mainstream, we propose a new model that represents spatial data as geometric objects and define an algebra consisting of GPU-friendly composable operators that operate over these objects. We demonstrate the expressiveness of the proposed algebra and present a proof-of-concept prototype that supports a subset of the operators, which shows that it is orders of magnitude faster than a CPU-based implementation and outperforms custom GPU-based approaches.

Original languageEnglish (US)
Title of host publicationSIGMOD 2020 - Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages1875-1885
Number of pages11
ISBN (Electronic)9781450367356
DOIs
StatePublished - Jun 14 2020
Event2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020 - Portland, United States
Duration: Jun 14 2020Jun 19 2020

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Conference

Conference2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020
CountryUnited States
CityPortland
Period6/14/206/19/20

Keywords

  • GPU processing
  • spatial query model

ASJC Scopus subject areas

  • Software
  • Information Systems

Fingerprint Dive into the research topics of 'A GPU-friendly Geometric Data Model and Algebra for Spatial Queries'. Together they form a unique fingerprint.

Cite this