  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 three-level 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 50-100 after fine tuning of
algorithms.
