Towards a Science Gateway for Bioinformatics: Experiences in the Brazilian System of High Performance Computing

Ocaña, Kary; Galheigo, Marcelo; Osthoff, Carla; Gadelha, Luiz M. R.; Gomes, Antônio Tadeu A.; Oliveira, Daniel de; Porto, Fábio; Vasconcelos, Ana Tereza Ribeiro de

doi:10.1109/ccgrid.2019.00082

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…Although previous research has focused on clustering analysis through sequential algorithms [45,49,82,83], the results described in Table 2 showed that the cluster's quality was competitive compared to the results described in the state of art [83,88,89]. Therefore, we can confirm the main idea in [53], which mentioned that it is possible to improve computational performance to reduce the execution time of the clustering algorithms. It was evident that much has been conducted to propose clustering algorithms capable of obtaining results according to the biological context, but little to improve their computational performance.…”

Section: Discussionmentioning

confidence: 72%

“…In this way, current research should focus on improving computational performance by creating efficient algorithms from the already effective ones. To overcome this problem, High-Performance Computing (HPC) mechanisms based on general-purpose Graphics Processing Units (GPUs) are starting to be used within this area [53][54][55], as they can considerably reduce the execution time required in a CPU and allow more intensive investigations of biological systems.…”

Section: Introductionmentioning

confidence: 99%

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Gene Expression Analysis through Parallel Non-Negative Matrix Factorization

2021

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Genetic expression analysis is a principal tool to explain the behavior of genes in an organism when exposed to different experimental conditions. In the state of art, many clustering algorithms have been proposed. It is overwhelming the amount of biological data whose high-dimensional structure exceeds mostly current computational architectures. The computational time and memory consumption optimization actually become decisive factors in choosing clustering algorithms. We propose a clustering algorithm based on Non-negative Matrix Factorization and K-means to reduce data dimensionality but whilst preserving the biological context and prioritizing gene selection, and it is implemented within parallel GPU-based environments through the CUDA library. A well-known dataset is used in our tests and the quality of the results is measured through the Rand and Accuracy Index. The results show an increase in the acceleration of 6.22× compared to the sequential version. The algorithm is competitive in the biological datasets analysis and it is invariant with respect to the classes number and the size of the gene expression matrix.

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Section: Discussionmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Gene Expression Analysis through Parallel Non-Negative Matrix Factorization

2021

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“…RNBio tem colaborações científicas com LNCC no desenvolvimento de portais ou chamados também de gateways científicos para bioinformática, como o Bioinfo-Portal (www.bioinfo.lncc.br). SDumont fornece a infraestrutura computacional combinando a adaptação de metodologias analíticas de Inteligência Artificial (IA) eficientes para análises de dados e execuções em larga escala de aplicativos e fluxos de trabalho especializados [Ocaña et al 2019a]. Os portais científicos são soluções de software de infraestrutura eletrônica que trazem a integração de dados reutilizáveis e técnicas especializadas por meio de servidores da Web, ao mesmo tempo em que escondem a complexidade de uso dos recursos de CAD subjacentes para comunidades de pesquisa.…”

Section: Portais Científicosunclassified

A Arquitetura do Supercomputador SDumont e os Desafios da Pesquisa Brasileira na Área de Computação de Alto Desempenho

Osthoff¹,

Herrera²,

Teixeira³

et al. 2020

Anais Da XI Escola Regional De Alto Desempenho De São Paulo (ERAD-SP 2020)

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O supercomputador Santos Dumont é o quarto maior da América Latina segundo a lista TOP500 com um total de 5.1 petaflops. Ele está hospedado no Laboratório Nacional de Computação Científica (Petrópolis, Brasil), uma unidade de pesquisa do Ministério da Ciência, Tecnologia e Inovação do Brasil. Neste trabalho apresentamos as principais caracterı́sticas do supercomputador, suas áreas e projetos de pesquisa envolvidos, software e portais cientı́ficos acoplados nele, trabalhos em andamento do nosso grupo para otimizar as execuções e informações gerais a usuários potenciais que pretendam submeter projetos nas chamadas para o uso do Santos Dumont.

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