TY - GEN
T1 - SPADE
T2 - 38th IEEE International Conference on Data Engineering, ICDE 2022
AU - Doraiswamy, Harish
AU - Freire, Juliana
N1 - Funding Information:
The novel approach to spatial query processing used by Spade not only opens new research opportunities in spatial query optimization and indexing, but also enables complex queries that were not possible before–e.g., accurate distance-based queries with respect to complex objects that are not supported in existing systems due to its complexity can be accomplished in Spade with a minimal overhead. Furthermore, with the recent Spark 3.x adding support for GPUs, we believe a significant performance boost can be obtained by integrating Spade with systems such as GeoSpark. Acknowledgements. This work was partially supported by the DARPA D3M program and NSF award IIS-2106888. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF and DARPA.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Given the massive growth in the volume of spatial data, there is a great need for systems that can efficiently evaluate spatial queries over large data sets. These queries are notoriously expensive using traditional database solutions. While faster response times can be attained through powerful clusters or servers with large main-memory, these options, due to cost and complexity, are out of reach to many data scientists and analysts making up the long tail. Graphics Processing Units (GPUs), which are now widely available even in commodity desktops and laptops, provide a cost-effective alternative to support high-performance computing, opening up new opportunities to the efficient evaluation of spatial queries. While GPU-based approaches proposed in the literature have shown great improvements in performance, they are tied to specific GPU hardware and only handle specific queries over fixed geometry types. In this paper we present SPADE, a GPU-powered spatial database engine that supports a rich set of spatial queries. We discuss the challenges involved in attaining efficient query evaluation over large datasets as well as portability across different GPU hardware, and how these are addressed in SPADE. We performed a detailed experimental evaluation to assess the effectiveness of the system for wide range of queries and datasets, and report results which show that SPADE is scalable and able to handle data larger than main-memory, and its performance on a laptop is on par with that other systems that require clusters or large-memory servers.
AB - Given the massive growth in the volume of spatial data, there is a great need for systems that can efficiently evaluate spatial queries over large data sets. These queries are notoriously expensive using traditional database solutions. While faster response times can be attained through powerful clusters or servers with large main-memory, these options, due to cost and complexity, are out of reach to many data scientists and analysts making up the long tail. Graphics Processing Units (GPUs), which are now widely available even in commodity desktops and laptops, provide a cost-effective alternative to support high-performance computing, opening up new opportunities to the efficient evaluation of spatial queries. While GPU-based approaches proposed in the literature have shown great improvements in performance, they are tied to specific GPU hardware and only handle specific queries over fixed geometry types. In this paper we present SPADE, a GPU-powered spatial database engine that supports a rich set of spatial queries. We discuss the challenges involved in attaining efficient query evaluation over large datasets as well as portability across different GPU hardware, and how these are addressed in SPADE. We performed a detailed experimental evaluation to assess the effectiveness of the system for wide range of queries and datasets, and report results which show that SPADE is scalable and able to handle data larger than main-memory, and its performance on a laptop is on par with that other systems that require clusters or large-memory servers.
KW - Geospatial queries
KW - GPU computing
UR - http://www.scopus.com/inward/record.url?scp=85136413320&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85136413320&partnerID=8YFLogxK
U2 - 10.1109/ICDE53745.2022.00245
DO - 10.1109/ICDE53745.2022.00245
M3 - Conference contribution
AN - SCOPUS:85136413320
T3 - Proceedings - International Conference on Data Engineering
SP - 2669
EP - 2681
BT - Proceedings - 2022 IEEE 38th International Conference on Data Engineering, ICDE 2022
PB - IEEE Computer Society
Y2 - 9 May 2022 through 12 May 2022
ER -