High performance computing architectures analysis for gene networks inference

Anderson Marco; Mario Gazziro; David Martins Jr

doi:10.5753/wscad.2019.8656

Anderson Marco Universidade Federal do ABC
Mario Gazziro Universidade Federal do ABC
David Martins Jr Universidade Federal do ABC

DOI: https://doi.org/10.5753/wscad.2019.8656

Resumo

A modelagem e inferência de sistemas biológicos é uma importante área de pesquisa em ciência da computação, possuindo caracterı́sticas fortemente interdisciplinares. Nesse contexto, a inferência de redes de regulação gênica e a análise da dinâmica da expressão gerada pelas suas funções de transição são problemas relevantes. Tais problemas demandam muito poder computacional, já que algoritmos que buscam pela solução ótima possuem complexidade de tempo exponencial. Esta complexidade de tempo faz com que muitas análises sejam realizadas em redes gênicas com dezenas de genes e com cada gene tendo poucos preditores. Porém, uma rede gênica de tamanho real tipicamente possui milhares de genes, com alguns desses genes podendo ser hubs por terem um grau de entrada (número de preditores) bem acima da média. Desta forma é necessário buscar meios de acelerar o processamento da inferência de redes gênicas. Este artigo mostra uma comparação entre GPUs e FPGAs, duas arquiteturas computacionais de alto desempenho, na realização Abstraction Level

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