Parallel implementation proposal of clustering algorithms in hardware

• Ano de defesa: 2020
• Autor(a) principal: Dias, Leonardo Alves
• Orientador(a): Fernandes, Marcelo Augusto Costa
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Rio Grande do Norte
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA ELÉTRICA E DE COMPUTAÇÃO
• País: Brasil
• Palavras-chave em Português: Massive Data sets; Data Clustering; Parallel Systems; Hardware
• Link de acesso: https://repositorio.ufrn.br/handle/123456789/31003

This work presents a study on data clustering algorithms implemented in dedicated hardware for applications in general, aiming to increase the processing speed. Clustering algorithms have been widely adopted to find patterns between data in different areas. However, these algorithms usually imply high processing complexity and, in addition, the amount of data currently stored is massive. Therefore, the need for high-throughput data processing has become even more critical, especially for real-time applications. One solution that has been adopted to increase processing speed is the use of parallel techniques implemented on dedicated hardware, which has proved to be more efficient compared to sequential systems. Therefore, this work proposes the fully parallel implementation of data clustering algorithms in hardware to optimize the processing time of systems in several areas, enabling applications for systems with a massive amount of data. A new proposal for implementations of the clustering algorithms K-means and Self-Organizing Maps are presented, together with an analysis of the results related to throughput and the hardware resource for different parameters, showing a speedup of millions of data points and connections updated per second. The implementations presented here point to a new direction associated with the implementation of clustering algorithms and can be used in other algorithms.