Proposta e Avaliação de um Cluster de Banana Pi Single Boards com NAS Parallel Benchmarks
Resumo
Clusters para programação paralela de alto desempenho podem ser um grande problema para projetos de pesquisas com baixo orçamento. Pensando nisso, esse artigo propõe um cluster de Banana Pi single boards como solução viável financeiramente para uso em pesquisa e ensino. O artigo apresenta também uma comparação com outro cluster baseado em Raspberry Pi desenvolvido no passado pelo mesmo grupo. Durante os testes, foi possível avaliar resultados que verificam o desempenho e escalabilidade de aplicações do NAS benchmarks. Um speedup máximo de 13.18 foi alcançado com uma eficiência de 165% com 8 núcleos para a carga CG de classe B.
Referências
Adnan, Z. Tahir, C. Yohannes, e Ariel. Performance evaluation of mini single board computer in hadoop big data cluster. IOP Conference Series: Materials Science and Engineering, 875 (1):012037, jun 2020. DOI: 10.1088/1757-899X/875/1/012037.
D. Bailey, E. Barszcz, J. Barton, D. Browning, R. Carter, L. Dagum, R. Fatoohi, P. Frederickson, T. Lasinski, R. Schreiber, H. Simon, V. Venkatakrishnan, e S. Weeratunga. The nas parallel benchmarks. The International Journal of Supercomputing Applications, 5(3):63–73, 1991. DOI: 10.1177/109434209100500306.
G. T. Castro, L. E. Zárate, C. N. Nobre, e H. C. Freitas. A fast parallel k-modes algorithm for clustering nucleotide sequences to predict translation initiation sites. Journal of Computational Biology, 26(5):442–456, 2019. DOI: 10.1089/cmb.2018.0245. PMID: 30785342.
B. Di Pierro e S. Hank. Cpu and gpu parallel efficiency of arm based single board computing cluster for cfd applications. Computers Fluids, 272:106187, 2024. ISSN 0045-7930. DOI: 10.1016/j.compfluid.2024.106187.
I.-C. Donca, O. P. Stan, M. Misaros, A. Stan, e L. Miclea. Comprehensive security for iot devices with kubernetes and raspberry pi cluster. Electronics, 13(9), 2024. ISSN 2079-9292. DOI: 10.3390/electronics13091613.
G. A. Garcia e H. C. Freitas. Avaliação de desempenho de um cluster raspberry pi com nas parallel benchmarks. In Workshop de Iniciação Científica do Simpósio em Sistemas Computacionais de Alto Desempenho, 2015.
Y. Huang. Parallel computing and its applications. In 2022 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), pages 715–718, 2022. DOI: 10.1109/ICAICA54878.2022.9844487.
A. Ignácio e W. Dias. Análise do desempenho computacional de algoritmos paralelizados com openmp e mpi executados em raspberry pi. In Anais Estendidos do XXIV Simpósio em Sistemas Computacionais de Alto Desempenho, pages 41–48, Porto Alegre, RS, Brasil, 2023. SBC. DOI: 10.5753/wscad_estendido.2023.235967.
M. R. Karim, O. Beyan, A. Zappa, I. G. Costa, D. Rebholz-Schuhmann, M. Cochez, e S. Decker. Deep learning-based clustering approaches for bioinformatics. Briefings in Bioinformatics, Volume 22, Issue 1, January 2021, Pages 393–415, 2020.
A. Kumari e A. Singh. Message passing interface: An overview. The IUP Journal of Computer Sciences, Vol. XIV, No. 2, April 2020, pp. 33-42, 2020.
E. Matos, E. Moreno, e K. Bispo. Machine learning and raspberry pi cluster for training and detecting skin cancer. In Proceedings of the 18th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,, pages 75–82. INSTICC, SciTePress, 2022. ISBN 978-989-758-613-2. DOI: 10.5220/0011575500003318.
Y. Oyanagi. Future of supercomputing. Journal of Computational and Applied Mathematics Volume 149, Issue 1, 1 December 2002, Pages 147-153, 2022.
A. Poenaru, W.-C. Lin, e S. McIntosh-Smith. A performance analysis of modern parallel programming models using a compute-bound application. In B. L. Chamberlain, A.-L. Varbanescu, H. Ltaief, e P. Luszczek, editors, High Performance Computing, pages 332–350, Cham, 2021. Springer International Publishing. ISBN 978-3-030-78713-4.
D. Qian. High performance computing: a brief review and prospects. National Science Review, Volume 3, Issue 1, March 2016, Page 16,, 2016.
L. M. Rodrigues, L. E. Zárate, C. N. Nobre, e H. C. Freitas. Parallel and distributed kmeans to identify the translation initiation site of proteins. In 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 1639–1645, 2012. DOI: 10.1109/ICSMC.2012.6377972.
J. Saffran, G. Garcia, M. A. Souza, P. H. Penna, M. Castro, L. F. W. Góes, e H. C. Freitas. A low-cost energy-efficient raspberry pi cluster for data mining algorithms. In Euro-Par 2016: Parallel Processing Workshops, pages 788–799, Cham, 2017. Springer International Publishing. ISBN 978-3-319-58943-5. DOI: 10.1007/978-3-319-58943-5_63.
G. Schryen. Speedup and efficiency of computational parallelization: A unifying approach and asymptotic analysis. Journal of Parallel and Distributed Computing, 187:104835, 2024. ISSN 0743-7315. DOI: 10.1016/j.jpdc.2023.104835.
M. A. Souza, P. H. Penna, M. M. Queiroz, A. D. Pereira, L. F. W. Góes, H. C. Freitas, M. Castro, P. O. Navaux, e J.-F. Méhaut. Cap bench: a benchmark suite for performance and energy evaluation of low-power many-core processors. Concurrency and Computation: Practice and Experience, 29(4):e3892, 2017. DOI: 10.1002/cpe.3892. e3892 cpe.3892.
C. S. Yeo, R. Buyya, H. Pourreza, e R. Eskicioglu. Cluster Computing: High-Performance, High-Availability, and High-Throughput Processing on a Network of Computers. Handbook of Nature-Inspired and Innovative Computing (pp.521-551), 2006.
D. Bailey, E. Barszcz, J. Barton, D. Browning, R. Carter, L. Dagum, R. Fatoohi, P. Frederickson, T. Lasinski, R. Schreiber, H. Simon, V. Venkatakrishnan, e S. Weeratunga. The nas parallel benchmarks. The International Journal of Supercomputing Applications, 5(3):63–73, 1991. DOI: 10.1177/109434209100500306.
G. T. Castro, L. E. Zárate, C. N. Nobre, e H. C. Freitas. A fast parallel k-modes algorithm for clustering nucleotide sequences to predict translation initiation sites. Journal of Computational Biology, 26(5):442–456, 2019. DOI: 10.1089/cmb.2018.0245. PMID: 30785342.
B. Di Pierro e S. Hank. Cpu and gpu parallel efficiency of arm based single board computing cluster for cfd applications. Computers Fluids, 272:106187, 2024. ISSN 0045-7930. DOI: 10.1016/j.compfluid.2024.106187.
I.-C. Donca, O. P. Stan, M. Misaros, A. Stan, e L. Miclea. Comprehensive security for iot devices with kubernetes and raspberry pi cluster. Electronics, 13(9), 2024. ISSN 2079-9292. DOI: 10.3390/electronics13091613.
G. A. Garcia e H. C. Freitas. Avaliação de desempenho de um cluster raspberry pi com nas parallel benchmarks. In Workshop de Iniciação Científica do Simpósio em Sistemas Computacionais de Alto Desempenho, 2015.
Y. Huang. Parallel computing and its applications. In 2022 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), pages 715–718, 2022. DOI: 10.1109/ICAICA54878.2022.9844487.
A. Ignácio e W. Dias. Análise do desempenho computacional de algoritmos paralelizados com openmp e mpi executados em raspberry pi. In Anais Estendidos do XXIV Simpósio em Sistemas Computacionais de Alto Desempenho, pages 41–48, Porto Alegre, RS, Brasil, 2023. SBC. DOI: 10.5753/wscad_estendido.2023.235967.
M. R. Karim, O. Beyan, A. Zappa, I. G. Costa, D. Rebholz-Schuhmann, M. Cochez, e S. Decker. Deep learning-based clustering approaches for bioinformatics. Briefings in Bioinformatics, Volume 22, Issue 1, January 2021, Pages 393–415, 2020.
A. Kumari e A. Singh. Message passing interface: An overview. The IUP Journal of Computer Sciences, Vol. XIV, No. 2, April 2020, pp. 33-42, 2020.
E. Matos, E. Moreno, e K. Bispo. Machine learning and raspberry pi cluster for training and detecting skin cancer. In Proceedings of the 18th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,, pages 75–82. INSTICC, SciTePress, 2022. ISBN 978-989-758-613-2. DOI: 10.5220/0011575500003318.
Y. Oyanagi. Future of supercomputing. Journal of Computational and Applied Mathematics Volume 149, Issue 1, 1 December 2002, Pages 147-153, 2022.
A. Poenaru, W.-C. Lin, e S. McIntosh-Smith. A performance analysis of modern parallel programming models using a compute-bound application. In B. L. Chamberlain, A.-L. Varbanescu, H. Ltaief, e P. Luszczek, editors, High Performance Computing, pages 332–350, Cham, 2021. Springer International Publishing. ISBN 978-3-030-78713-4.
D. Qian. High performance computing: a brief review and prospects. National Science Review, Volume 3, Issue 1, March 2016, Page 16,, 2016.
L. M. Rodrigues, L. E. Zárate, C. N. Nobre, e H. C. Freitas. Parallel and distributed kmeans to identify the translation initiation site of proteins. In 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 1639–1645, 2012. DOI: 10.1109/ICSMC.2012.6377972.
J. Saffran, G. Garcia, M. A. Souza, P. H. Penna, M. Castro, L. F. W. Góes, e H. C. Freitas. A low-cost energy-efficient raspberry pi cluster for data mining algorithms. In Euro-Par 2016: Parallel Processing Workshops, pages 788–799, Cham, 2017. Springer International Publishing. ISBN 978-3-319-58943-5. DOI: 10.1007/978-3-319-58943-5_63.
G. Schryen. Speedup and efficiency of computational parallelization: A unifying approach and asymptotic analysis. Journal of Parallel and Distributed Computing, 187:104835, 2024. ISSN 0743-7315. DOI: 10.1016/j.jpdc.2023.104835.
M. A. Souza, P. H. Penna, M. M. Queiroz, A. D. Pereira, L. F. W. Góes, H. C. Freitas, M. Castro, P. O. Navaux, e J.-F. Méhaut. Cap bench: a benchmark suite for performance and energy evaluation of low-power many-core processors. Concurrency and Computation: Practice and Experience, 29(4):e3892, 2017. DOI: 10.1002/cpe.3892. e3892 cpe.3892.
C. S. Yeo, R. Buyya, H. Pourreza, e R. Eskicioglu. Cluster Computing: High-Performance, High-Availability, and High-Throughput Processing on a Network of Computers. Handbook of Nature-Inspired and Innovative Computing (pp.521-551), 2006.
Publicado
23/10/2024
Como Citar
SILVA, Marcos Ani Cury Vinagre; FREITAS, Henrique Cota de.
Proposta e Avaliação de um Cluster de Banana Pi Single Boards com NAS Parallel Benchmarks. In: WORKSHOP DE INICIAÇÃO CIENTÍFICA - SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 25. , 2024, São Carlos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 49-56.
DOI: https://doi.org/10.5753/sscad_estendido.2024.244722.
