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Multilevel parallelism scheme in a genetic algorithm applied to cardiac models with mass–spring systems

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Abstract

In this work we propose an adaptive parallel genetic algorithm, which is able to automatically choose which loops should run in parallel in order to balance the workload sharing and the extra cost to manage threads’ forks and joins. Our genetic algorithm finds parameters to our cardiac simulator based on mass–spring systems and cellular automata, in order to reproduce a cycle of contraction and relaxation of the human left ventricle. The ventricle geometry was obtained by extracting information from MRI data. Our multilevel parallelism scheme decreased the execution time up to 12 % and the GA adjusts the cardiac model so that it reproduced a cardiac cycle. The advantages and drawbacks of our method are discussed as well as its limitations.

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References

  1. Fenton FH, Cherry EM, Hastings HM, Evans SJ (2002) Multiple mechanisms of spiral wave breakup in a model of cardiac electrical activity. Chaos 12:852

    Article  Google Scholar 

  2. Vigmond EJ, Weber dos Santos R, Prassl AJ, Deo M, Plank G (2008) Solvers for the cardiac bidomain equations. Prog Biophys Mol Biol 96(1):3–18

    Article  Google Scholar 

  3. Weber dos Santos R, Plank G, Bauer S, Vigmond EJ (2004) Parallel multigrid preconditioner for the cardiac bidomain model. Biomed Eng IEEE Trans 51(11):1960–1968

    Article  Google Scholar 

  4. Atienza FA, Carrin AG, Alberola JR, lvarez JLR, Muoz JJS, Sichez JM, Chvarri MV (2005) A probabilistic model of cardiac electrical activity based on a cellular automata system. Rev Esp Cardiol 58(1):41–47

    Article  Google Scholar 

  5. Amorim RM, Campos RS, Lobosco M, Jacob C, Santos RW (2012) An electro-mechanical cardiac simulator based on cellular automata and mass-spring models. Lect Notes Comput Sci 7495:434–443

    Article  Google Scholar 

  6. Campos RS, Amorim RM, de Oliveira BL, Rocha BM, Sundnes J, Barra LPS, Lobosco M, Santos RW (2013) 3D heart modeling with cellular automata, mass-spring system and CUDA. Lect Notes Comput Sci 7979:296–309

    Article  Google Scholar 

  7. Campos RS, Lobosco M, Santos RW (2014) A GPU-based heart simulator with mass-spring systems and cellular automaton. J Supercomput 69:1–8

    Article  Google Scholar 

  8. Campos RS, Rocha BM, Barra LPS, Lobosco M, Santos RW (2015) A parallel genetic algorithm to adjust a cardiac model based on cellular automaton and mass-spring systems. Lect Notes Comput Sci 9251:149–163

    Article  Google Scholar 

  9. Pennacchio M, Simoncini ANDV (2011) Fast structured AMG preconditioning for the bidomain model in electrocardiology. SIAM J Sci Comput 33:721–745

    Article  MathSciNet  MATH  Google Scholar 

  10. Rodrigues PF, Schmal TR, Gomes JM, Rocha BM, Santos RW (2015) Patient-specific left ventricle mesh generation using the Bull’s Eye of the wall thickness measurements from medical images. IFMBE Proc 49:393–396

    Article  Google Scholar 

  11. Koch H, Bousseljot RD, Kosch O, Jahnke C, Paetsch I, Fleck E, Schnackenburg B (2011) A reference dataset for verifying numerical electrophysiological heart models. BioMed Eng 10(1):11 (Online)

    Google Scholar 

  12. Rossi S, Ruiz-Baier R, Pavarino LF, Quarteroni A (2012) Orthotropic active strain models for the numerical simulation of cardiac biomechanics. Int J Numer Method Biomed Eng 28(6–7):761–788

    Article  MathSciNet  Google Scholar 

  13. Jarrouse O (2011) Modified mass-spring system for physically based deformation modeling. Ph.D. thesis, Karlsruher Instituts fur Technologie

  14. Bora C, Serinagaoglu Y, Tonuk E (2010) Electromechanical heart tissue model using cellular automaton. Biomedical Engineering Meeting (BIYOMUT), 2010 15th National, pp 1–4

Download references

Acknowledgments

The authors thank CAPES, CNPq, FAPEMIG and UFJF for supporting this work.

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Authors and Affiliations

  1. Programa de Pós-Graduação em Modelagem Computacional, Universidade Federal de Juiz de Fora, Juiz de Fora, MG, Brazil

    Ricardo Silva Campos, Bernardo Martins Rocha, Marcelo Lobosco & Rodrigo Weber dos Santos

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  1. Ricardo Silva Campos
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  2. Bernardo Martins Rocha
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  3. Marcelo Lobosco
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  4. Rodrigo Weber dos Santos
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Correspondence to Rodrigo Weber dos Santos.

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Campos, R.S., Rocha, B.M., Lobosco, M. et al. Multilevel parallelism scheme in a genetic algorithm applied to cardiac models with mass–spring systems. J Supercomput 73, 609–623 (2017). https://doi.org/10.1007/s11227-016-1798-4

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  • DOI: https://doi.org/10.1007/s11227-016-1798-4

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