kNN Query Processing in Metric Spaces Using GPUs

67 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Billion-Scale Similarity Search with GPUs (2017, background): “Heaps can be similarly implemented on a GPU [7].”
Efficient Computation of k-Nearest Neighbour Graphs for Large High-Dimensional Data Sets on GPU Clusters (2013, methodology): “Popular hashing schemes include the Locality Sensitive Hashing (LSH)[11], Z-Morton Curve based hashing [9], and Hilbert space filling curve hashing [4] .”
Parallel search of k-nearest neighbors with synchronous operations (2012, methodology): “In utilizing architectural means, many efforts have used GPUs (especially, those by NVIDIA) as many-core parallel processing units, available and affordable, with increased and increasing support of application interfaces [15], [16], [17], 1http://developer.nvidia.com/cuda-downloads [18], [19].”
Efficient Selection Algorithm for Fast k-NN Search on GPUs (2015, background): “1a, its invariant is that a parent node should be larger than its child nodes.”