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Performance evaluation of GPU parallelization, space-time adaptive algorithms, and their combination for simulating cardiac electrophysiology.
Sachetto Oliveira, Rafael; Martins Rocha, Bernardo; Burgarelli, Denise; Meira, Wagner; Constantinides, Christakis; Weber Dos Santos, Rodrigo.
Afiliação
  • Sachetto Oliveira R; Departamento de Ciência da Computação, Universidade Federal de São João de Rei, São João del-rei MG, Brazil.
  • Martins Rocha B; Departamento de Ciência da Computação e Programa em Modelagem Computacional, Universidade Federal de Juiz de Fora, Juiz de Fora, MG, Brazil.
  • Burgarelli D; Departamento de Matemática, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
  • Meira W; Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
  • Constantinides C; Department of Cardiovascular Medicine, U. Oxford, Oxford, UK.
  • Weber Dos Santos R; Departamento de Ciência da Computação e Programa em Modelagem Computacional, Universidade Federal de Juiz de Fora, Juiz de Fora, MG, Brazil.
Int J Numer Method Biomed Eng ; 34(2)2018 02.
Article em En | MEDLINE | ID: mdl-28636811
The use of computer models as a tool for the study and understanding of the complex phenomena of cardiac electrophysiology has attained increased importance nowadays. At the same time, the increased complexity of the biophysical processes translates into complex computational and mathematical models. To speed up cardiac simulations and to allow more precise and realistic uses, 2 different techniques have been traditionally exploited: parallel computing and sophisticated numerical methods. In this work, we combine a modern parallel computing technique based on multicore and graphics processing units (GPUs) and a sophisticated numerical method based on a new space-time adaptive algorithm. We evaluate each technique alone and in different combinations: multicore and GPU, multicore and GPU and space adaptivity, multicore and GPU and space adaptivity and time adaptivity. All the techniques and combinations were evaluated under different scenarios: 3D simulations on slabs, 3D simulations on a ventricular mouse mesh, ie, complex geometry, sinus-rhythm, and arrhythmic conditions. Our results suggest that multicore and GPU accelerate the simulations by an approximate factor of 33×, whereas the speedups attained by the space-time adaptive algorithms were approximately 48. Nevertheless, by combining all the techniques, we obtained speedups that ranged between 165 and 498. The tested methods were able to reduce the execution time of a simulation by more than 498× for a complex cellular model in a slab geometry and by 165× in a realistic heart geometry simulating spiral waves. The proposed methods will allow faster and more realistic simulations in a feasible time with no significant loss of accuracy.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Gráficos por Computador / Eletrofisiologia Cardíaca Tipo de estudo: Prognostic_studies Limite: Animals Idioma: En Revista: Int J Numer Method Biomed Eng Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Brasil País de publicação: Reino Unido
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Gráficos por Computador / Eletrofisiologia Cardíaca Tipo de estudo: Prognostic_studies Limite: Animals Idioma: En Revista: Int J Numer Method Biomed Eng Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Brasil País de publicação: Reino Unido