2015 IEEE International Conference on Data Mining (ICDM) (2015)
Atlantic City, NJ, USA
Nov. 14, 2015 to Nov. 17, 2015
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2015.125
In recent years, the use of Graphics Processing Units (GPUs) for data mining tasks has become popular. With modern processors integrating both CPUs and GPUs, it is also important to consider what tasks benefit from GPU processing and which do not, and apply a heterogeneous processing approach to improve the efficiency where applicable. Similarity search, also known as k-nearest neighbor search, is a key part of data mining applications and is used also extensively in applications such as multimedia search, where only a small subset of possible results are used. Our contribution is a new exact kNN algorithm with a compressed partial heapsort that outperforms other state-of-the-art exact kNN algorithms by leveraging both the GPU and CPU.
Graphics processing units, Sorting, Instruction sets, Force, Data mining, Approximation methods
T. Matsumoto and M. L. Yiu, "Accelerating Exact Similarity Search on CPU-GPU Systems," 2015 IEEE International Conference on Data Mining (ICDM), Atlantic City, NJ, USA, 2015, pp. 320-329.