# Parallel fuzzy minimals on GPU
## 3 Parallel FM
### Parallel FM on GPU
On the other hand, for the PFMMPI implementation, although each cluster's node executes a single MPI process, the CPU is multicore. Moreover, the major bottleneck with GPU is the data transfers between CPU and GPU memories. Therefore, the speedup achieved in the ellipsis test is higher in the case of GPU, as shown in Figure 10.

## 5 Conclusions

The results of this paper are based on the parallel version of the FM algorithm on GPU (PFMGPU). This approach provided high performance at a low computational cost. More precisely, the use of GPUs allowed the parallelization by a reasonably low-cost parallel hardware, without variations in the accuracy of FM clustering algorithm. Figure 10: Speedups for PFMGPU (**top**) and PFMMPI (**bottom**). The benchmarks studied in this paper proved that PFMGPU can solve clustering problems several times faster than in a classical CPU. In particular, we achieved a solution at least 50 times faster than the sequential execution in hypothesis of eight partitions. The major drawback of our solution is that each partition must have at least 10% of the data. Thus, we have a strict limitation on the number of partitions. Nevertheless, PFMGPU provided better results than a conventional parallelization via MPI (PFMMPI). This showed that even low-end GPUs can offer better performance. We propose that further research should be undertaken in two directions. Firstly, it is relevant to study the influence of partitions smaller than 10% in the cluster accuracy. Secondly, we need to deal with forms to improve the parallelization inside a partition. All authors contributed equally to this article. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors wish to express his gratitude to the reviewers for their constructive comments and suggestions. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:

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