Search Machine Learning Repository: @inproceedings{icml2014c1_gieseke14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Buffer k-d Trees: Processing Massive Nearest Neighbor Queries on GPUs},
    Url = {http://jmlr.org/proceedings/papers/v32/gieseke14.pdf},
    Abstract = {We present a new approach for combining k-d trees and graphics processing units for nearest neighbor search. It is well known that a direct combination of these tools leads to a non-satisfying performance due to conditional computations and suboptimal memory accesses. To alleviate these problems, we propose a variant of the classical k-d tree data structure, called buffer k-d tree, which can be used to reorganize the search. Our experiments show that we can take advantage of both the hierarchical subdivision induced by k-d trees and the huge computational resources provided by today's many-core devices. We demonstrate the potential of our approach in astronomy, where hundreds of million nearest neighbor queries have to be processed.},
    Author = {Fabian Gieseke and Justin Heinermann and Cosmin Oancea and Christian Igel},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {172-180}
   }