ACM Computing Surveys (CSUR)
An Effective Clustering Algorithm to Index High Dimensional Metric Spaces
SPIRE '00 Proceedings of the Seventh International Symposium on String Processing Information Retrieval (SPIRE'00)
A compact space decomposition for effective metric indexing
Pattern Recognition Letters
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Generic similarity search engine demonstrated by an image retrieval application
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Scheduling Metric-Space Queries Processing on Multi-Core Processors
PDP '10 Proceedings of the 2010 18th Euromicro Conference on Parallel, Distributed and Network-based Processing
Distributing a Metric-Space Search Index onto Processors
ICPP '10 Proceedings of the 2010 39th International Conference on Parallel Processing
Fully dynamic metric access methods based on hyperplane partitioning
Information Systems
Multi feature indexing network MUFIN for similarity search applications
SOFSEM'12 Proceedings of the 38th international conference on Current Trends in Theory and Practice of Computer Science
Range Query Processing in a Multi-GPU Environment
ISPA '12 Proceedings of the 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications
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The field of similarity search on metric spaces has been widely studied in the last years, mainly because it has proven suitable for a number of application domains such as multimedia retrieval and computational biology, just to name a few. To achieve efficient query execution throughput, it is essential to exploit the intrinsic parallelism in respective search algorithms. Many strategies have been proposed in the literature to parallelize these algorithms either on shared or distributed memory multiprocessor systems. More recently, GPUs have been proposed to evaluate similarity queries for small indexes that fit completely in GPU's memory. However, most of the real databases in production are much larger. In this paper, we propose multi-GPU metric space techniques that are capable to perform similarity search in datasets large enough not to fit in memory of GPUs. Specifically, we implemented a hybrid algorithm which makes use of CPU-cores and GPUs in a pipeline. We also present a hierarchical multi-level index named List of Superclusters (LSC), with suitable properties for memory transfer in a GPU.