A GPU-Based Implementation for Range Queries on Spaghettis Data Structure
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part I
kNN query processing in metric spaces using GPUs
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
Load Balancing Query Processing in Metric-Space Similarity Search
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
Multi-level clustering on metric spaces using a Multi-GPU platform
Euro-Par'13 Proceedings of the 19th international conference on Parallel Processing
Range query processing on single and multi GPU environments
Computers and Electrical Engineering
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This paper proposes a strategy to organize metricspace query processing in multi-core search nodes as understood in the context of search engines running on clusters of computers. The strategy is applied in each search node to process all active queries visiting the node as part of their solution which, in general, for each query is computed from the contribution of each search node. When query traffic is high enough, the proposed strategy assigns one thread to each query and lets them work in a fully asynchronous manner. When query traffic is moderate or low, some threads start to idle so they are put to work on queries being processed by other threads. The strategy solves the associated synchronization problem among threads by switching query processing into a bulk-synchronous mode of operation. This simplifies the dynamic re-organization of threads and overheads are very small with the advantage that the overall work-load is evenly distributed across all threads.