Parallel algorithms for problems of cluster analysis with very large amount of data
Abstract
In this paper we solve on GPUs massive problems with large amount of data, which are not appropriate for solution with the SIMD technology. For the given problem we consider a threelevel parallelization. The multithreading of CPU is used at the top level and graphic processors for massive computing. For solving problems of cluster analysis on GPUs the nearest neighbor method (NNM) is developed. This algorithm allows us to handle up to 2 millions records with number of features up to 25. Since sequential and parallel algorithms are fundamentally different, it is difficult to compare the computation times. However, some comparisons are made. The gain in the computing time is about 10 times. We plan to increase this factor up to 50100 after fine tuning of algorithms.
 Publication:

arXiv eprints
 Pub Date:
 February 2014
 arXiv:
 arXiv:1402.3789
 Bibcode:
 2014arXiv1402.3789L
 Keywords:

 Computer Science  Distributed;
 Parallel;
 and Cluster Computing;
 91C20;
 68W10;
 6207;
 D.1.3;
 G.1.0;
 G.4;
 H.3.3;
 I.5.3