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Generalized Neighbor Search using Commodity Hardware Acceleration
Tree-based Nearest Neighbor Search (NNS) is hard to parallelize on GPUs.
However, newer Nvidia GPUs are equipped with Ray Tracing (RT) cores that can
build a spatial tree called Bounding Volume Hierarchy (BVH) to accelerate
graphics rendering. Recent work proposed using RT cores to implement NNS, but
they all have a hardware-imposed constraint on the type of distance metric,
which is the Euclidean distance. We propose and implement two approaches for
generalized distance computations: filter-refine, and monotone transformation,
each of which allows non-euclidean nearest neighbor queries to be performed in
terms of Euclidean distances. We find that our reductions improve the time
taken to perform distance computations during the search, thereby improving the
overall performance of the NNS
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