Accelerating Medoids-based clustering with the Intel Many Integrated Core architecture

The Partition Around Medoids (PAM) is a variation of well known k-Means clustering algorithm where center of each cluster should be chosen as an object of clustered set of objects. PAM is used in a wide spectrum of applications, e.g. text analysis, bioinformatics, intelligent transportation systems, etc. There are approaches to speed up k-Means and PAM algorithms by means of graphic accelerators but there none for accelerators based on the Intel Many Integrated Core architecture. This paper presents a parallel version of PAM for the Intel Xeon Phi many-core coprocessor. Parallelization is based on the OpenMP technology. Loop operations are adapted to provide vectorization. Distance matrix is precomputed and stored in the coprocessor´s memory. Experimental results are presented and confirm the efficiency of the algorithm.