Architectural implications for spatial object association algorithms

Authors:
Vijay S. Kumar;Tahsin Kurc;Joel Saltz;Ghaleb Abdulla;Scott R. Kohn;Celeste Matarazzo
Affiliations:
Department of Computer Science and Engineering, The Ohio State University, USA;Center for Comprehensive Informatics, Emory University, USA;Center for Comprehensive Informatics, Emory University, USA;Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, USA;Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, USA;Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, USA
Venue:
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Year:
2009

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Abstract

Spatial object association, also referred to as crossmatch of spatial datasets, is the problem of identifying and comparing objects in two or more datasets based on their positions in a common spatial coordinate system. In this work, we evaluate two crossmatch algorithms that are used for astronomical sky surveys, on the following database system architecture configurations: (1) Netezza Performance Server®, a parallel database system with active disk style processing capabilities, (2) MySQL Cluster, a high-throughput network database system, and (3) a hybrid configuration consisting of a collection of independent database system instances with data replication support. Our evaluation provides insights about how architectural characteristics of these systems affect the performance of the spatial crossmatch algorithms. We conducted our study using real use-case scenarios borrowed from a large-scale astronomy application known as the Large Synoptic Survey Telescope (LSST).