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A Nearest Neighbor Data Structure for Graphics Hardware


Cayton,  L
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Cayton, L. (2010). A Nearest Neighbor Data Structure for Graphics Hardware. In First International Workshop on Accelerating Data Management Systems Using Modern Processor and Storage Architectures (ADMS 2010) (pp. 1-6).

Nearest neighbor search is a core computational task in database systems and throughout data analysis. It is also a major computational bottleneck, and hence an enormous body of research has been devoted to data structures and algorithms for accelerating the task. Recent advances in graphics hardware provide tantalizing speedups on a variety of tasks and suggest an alternate approach to the problem: simply run brute force search on a massively parallel sys- tem. In this paper we marry the approaches with a novel data structure that can effectively make use of parallel systems such as graphics cards. The architectural complexities of graphics hardware - the high degree of parallelism, the small amount of memory relative to instruction throughput, and the single instruction, multiple data design- present significant challenges for data structure design. Furthermore, the brute force approach applies perfectly to graphics hardware, leading one to question whether an intelligent algorithm or data structure can even hope to outperform this basic approach. Despite these challenges and misgivings, we demonstrate that our data structure - termed a Random Ball Cover - provides significant speedups over the GPU- based brute force approach.