Comparing high performance techniques for the automatic generation of efficient solvers of cardiac cell models

Campos, Ricardo Silva; Campos, Fernanda; Gomes, Johnny Moreira; Barbosa, Ciro de Barros; Lobosco, Marcelo; Santos, Rodrigo Weber dos

doi:10.1007/s00607-012-0268-y

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…However, stability issues demand very small time‐steps to be used. To overcome this problem, we used an adaptive time‐step Euler method, presented in Campos et al…”

Section: Methodsmentioning

confidence: 99%

“…However, stability issues demand very small time-steps to be used. To overcome this problem, we used an adaptive time-step Euler method, presented in Campos et al 21 The objective of this method is to adapt the time-step, trying to reduce it in regions where the small time-step is really needed, and increase it otherwise. For a system of ODEs with k variables, including the transmembrane potential V and the state variables , denoted by y j , for j = 1, … , k, the method used in this work can be described by the following formulation:…”

Section: Adaptive Time-step Methods For Solving Odesmentioning

confidence: 99%

“…Otherwise, the adaptive method obtains a new time‐step and follows to the next iteration n +1. More information about this adaptive method can be found in Campos et al…”

Section: Methodsmentioning

confidence: 99%

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Performance evaluation of GPU parallelization, space‐time adaptive algorithms, and their combination for simulating cardiac electrophysiology

Oliveira

Rocha

Burgarelli

et al. 2017

Numer Methods Biomed Eng

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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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“…However, stability issues demand very small time‐steps to be used. To overcome this problem, we used an adaptive time‐step Euler method, presented in Campos et al…”

Section: Methodsmentioning

confidence: 99%

Section: Adaptive Time-step Methods For Solving Odesmentioning

confidence: 99%