high performance computing on graphics processing units: hgpu.org

In data mining applications, one of the useful algorithms for classification is the kNN algorithm. The kNN search has a wide usage in many research and industrial domains like 3-dimensional object rendering, content-based image retrieval, statistics, biology (gene classification), etc. In spite of some improvements in the last decades, the computation time required by the kNN search remains the bottleneck for kNN classification, especially in high dimensional spaces. This bottleneck has created the necessity of the parallel kNN on commodity hardware. GPU and OpenCL architecture are the low cost high performance solutions for parallelising the kNN classifier. In regard to this, we have designed, implemented our proposed parallel kNN model to improve upon performance bottleneck issue of kNN algorithm.