Geodesic
A parallel data clustering algorithm for Intel MIC accelerators
Numerical methods and programming, Tome 20 (2019) no. 2, pp. 104-115.

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The PAM (Partitioning Around Medoids) is a partitioning clustering algorithm where each cluster is represented by an object from the input dataset (called a medoid). The medoid-based clustering is used in a wide range of applications: the segmentation of medical and satellite images, the analysis of DNA microarrays and texts, etc. Currently, there are parallel implementations of PAM for GPU and FPGA systems, but not for Intel Many Integrated Core (MIC) accelerators. In this paper, we propose a novel parallel PhiPAM clustering algorithm for Intel MIC systems. Computations are parallelized by the OpenMP technology. The algorithm exploits a sophisticated memory data layout and loop tiling technique, which allows one to efficiently vectorize computations with Intel MIC. Experiments performed on real data sets show a good scalability of the algorithm.
Keywords: OpenMP, Intel Xeon Phi, clustering, parallel algorithm, OpenMP, Intel Xeon Phi, data layout, vectorization of computations.
Mots-clés : medoid 
@article{VMP_2019_20_2_a2,
     author = {T. V. Rechkalov and M. L. Tsymbler},
     title = {A parallel data clustering algorithm for {Intel} {MIC} accelerators},
     journal = {Numerical methods and programming},
     pages = {104--115},
     publisher = {mathdoc},
     volume = {20},
     number = {2},
     year = {2019},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/VMP_2019_20_2_a2/}
}
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T. V. Rechkalov; M. L. Tsymbler. A parallel data clustering algorithm for Intel MIC accelerators. Numerical methods and programming, Tome 20 (2019) no. 2, pp. 104-115. http://geodesic.mathdoc.fr/item/VMP_2019_20_2_a2/