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  1. Proceedings of the ACM SIGSPATIAL Second International Workshop on High Performance and Distributed Geographic Information Systems (HPDGIS '11)
  2. Accelerating SIFT on hybrid clusters
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Accelerating SIFT on hybrid clusters
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Accelerating SIFT on hybrid clusters

Content Provider ACM Digital Library
Author Apon, Amy Warn, Seth Cothren, Jackson
Abstract We describe an approach to parallelizing SIFT and other scale-space-based feature transformation algorithms. By partitioning the workload in a novel fashion, our approach can take advantage of all forms of parallelism: the shared-memory parallelism of threaded programming, the distributed-memory approach of cluster programming, and GPU-based acceleration. Also described is an implementation of this approach called SOHC, or SIFT on hybrid clusters, which can take advantage of hybrid clusters to accelerate the transformation of arbitrarily large images into sets of features. SOHC is both portable and scalable: it can run on systems ranging from a desktop without any GPU hardware, to a cluster of multi-GPU nodes, with the only difference being time to complete the extraction. It is the only implementation of SIFT capable of operating directly (i.e. without dropping features at tile boundaries) on gigapixel-sized images often encountered in geospatial applications.
Starting Page 2
Ending Page 9
Page Count 8
File Format PDF
ISBN 9781450310406
DOI 10.1145/2070770.2070771
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2011-11-01
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Surf Sift Feature transformation Scale space Image matching Hybrid clusters Parallelization Gloh
Content Type Text
Resource Type Article
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