Accelerating Medoids-based clustering with the Intel Many Integrated Core architecture
Timofey Rechkalov, Mikhail L. Zymbler · 2015
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.