Abstract
Clustering is a classification method that organizes objects into groups based on their similarity. Data clustering can extract valuable information, such as human behavior, trends, and so on, from large datasets by using either hard or fuzzy approaches. However, this is a time-consuming problem due to the increasing volumes of data collected. In this context, sequential executions are not feasible and their parallelization is mandatory to complete the process in an acceptable time. Parallelization requires redesigning algorithms to take advantage of massively parallel platforms. In this paper we propose a novel parallel implementation of the fuzzy minimals algorithm on graphics processing unit as a high-performance low-cost solution for common clustering issues. The performance of this implementation is compared with an equivalent algorithm based on the message passing interface. Numerical simulations show that the proposed solution on graphics processing unit can achieve high performances with regards to the cost-accuracy ratio.
Highlights
In this paper we propose a novel parallel implementation of the fuzzy minimals algorithm on graphics processing unit as a high-performance low-cost solution for common clustering issues
Numerical simulations show that the proposed solution on graphics processing unit can achieve high performances with regards to the cost-accuracy ratio
Our parallel implementation using MPI is similar in spirit to that based on GPU
Summary
The most adopted fuzzy clustering algorithm has an extensive literature about its parallelization [15,16] These proposals do not change the requirements of this algorithm, i.e., a prior knowledge about the number of groups in the dataset. This has an exponential impact on the performance with the problem size, since several executions are needed to get the sought solution. These improvements, in a relatively short period of time, became the driving force behind thousands heavy computing problems Some of these applications based on fuzzy sets, provides solutions for image segmentation to help medical doctors diagnosing illnesses, simulation of wildfires, modeling geological phenomena, etc.
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