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

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• 作者：Ricardo Silva Campos (1)
  Fernando Otaviano Campos (1) (2)
  Johnny Moreira Gomes (1)
  Ciro de Barros Barbosa (1)
  Marcelo Lobosco (1)
  Rodrigo Weber dos Santos (1)
  
• 关键词：Cardiac electrophysiology ; Cardiac modeling ; Parallel programming ; Numerical methods ; Automatic code generation ; CellML ; 92 ; 08 Computational methods ; 92C05 Biophysics ; 92C30 Physiology (general) ; 92C37 Cell biology ; 65L05 Initial value problems ; 68N20 Compilers and interpreters
• 刊名：Computing
• 出版年：2013
• 出版时间：May 2013
• 年：2013
• 卷：95
• 期：1-supp
• 页码：639-660
• 全文大小：881 KB
• 参考文献：1. Pycml—cellml tools in python (2011). https://chaste.comlab.ox.ac.uk/cellml/
  2. Sundials (2011). https://computation.llnl.gov/casc/sundials/main.html
  3. W3c math home (2011). http://www.w3.org/Math/
  4. Barbosa CB, Santos RW, Amorim R, Ciuffo LN, Manfroi F, Oliveira RS, Campos FO (2006) A transformation tool for ODE based models. Lecture Notes Comput Sci 3991:69-5
  5. Bondarenko VE, Szigeti GP, Bett GCL, Kim SJ, Rasmusson RL (2004) A computer model of the action potential of the mouse ventricular myocytes. Am J Physiol 287:H1378–H1403
  6. Campos RS, Amorim RM, Costa CM, de Oliveira BL, de Barros Barbosa C, Sundnes J, dos Santos RW (2009) Approaching cardiac modeling challenges to computer science with CellML-based web tools. Future Gener Comput Syst 26(3):462470
  7. Campos RS, Lobosco M, dos Santos RW (2011) Adaptive time step for cardiac myocyte models. Proceedings of the International Conference on Computational Science, ICCS. Procedia Computer Science 4:1092-100. doi:10.1016/j.procs.2011.04.116
  8. Chandra R, Dagum L, Kohr D, McDonald DMJ, Menon R (2001) Parallel programming in OpenMP. Morgan Kaufmann Publishers, Burlington
  9. Cohen SD, Hindmarsh AC (1996) CVODE, a stiff/nonstiff ODE solver in C. Comput Phys 10(2): 138-43
  10. Cooper J, McKeever S, Garny A (2006) On the application of partial evaluation to the optimisation of cardiac electrophysiological simulations. Proceedings of the 2006 ACM SIGPLAN symposium on Partial evaluation and semantics-based program manipulation, p 1220. doi:10.1145/1111542.1111546
  11. Cooper JP (2009) Automatic validation and optimisation of biological models. Ph.D. thesis, Oxford University. http://ora.ouls.ox.ac.uk/objects/uuid:24b96d62-b47c-458d-9dff-79b27dbdc9f2
  12. Cormen TH, Leiserson CE, Rivest RL, Stein C (2001) Introduction to algorithms, 2nd edn. The MIT Press, Cambridge
  13. Garny A, Kohl P, Hunter PJ, Boyett MR, Noble D (2003) One-dimensional rabbit sinoatrial node models: benefits and limitations. J Cardiovasc Electrophysiol 14:S121–S132 CrossRef
  14. Garny A, Nickerson DP, Cooper J, dos Santos RW, Miller AK, McKeever S, Nielsen, PMF, Hunter PJ (2008) Cellml and associated tools and techniques. Philos Trans Roy Soc A 366:3017-043. doi:10.1098/rsta.2008.0094
  15. Geselowitz D, Miller W (1983) A bidomain model for anisotropic cardiac muscle. Ann Biomed Eng 11(3-):191-06 CrossRef
  16. Hodgkin A, Huxley A (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117:500-44
  17. Luo CH, Rudy Y (1991) A model of the ventricular cardiac action potential: depolarization, repolarization, and their interaction. Circ Res 68(6):1501-526. doi:10.1161/01.RES.68.6.1501
  18. Martins D, Campos FO, Ciuffo LN, Oliveira RS, Amorim RM, Fonseca Vieira V, Ebecken NFF, de Barros Barbosa C, dos Santos RW (2007) A computational framework for cardiac modeling based on distributed computing and web applications. Lecture Notes Comput Sci 4395:544-55 CrossRef
  19. Mattson TG, Sanders BA, Massingill BL (2005) Patterns for parallel programming. Pearson Education, USA
  20. Noble D, Varghese A, Kohl P, Noble P (1998) Improved guinea-pig ventricular cell model incorporating a diadic space, IKr and IKs, and length- and tension-dependent processes. J Cardiol 14:123-34
  21. Rush S, Larsen H (1978) A practical algorithm for solving dynamic membrane equations. IEEE Trans Biomed Eng 25(4):389-92 CrossRef
  22. Spiteri R, Dean R (2008) On the performance of an implicit–explicit Runge–Kutta method in models of cardiac electrical activity. IEEE Trans Biomed Eng 55(5):1488-495 CrossRef
  23. Sundnes J, Artebrant R, Skavhaug O, Tveito A (2009) A second-order algorithm for solving dynamic cell membrane equations. IEEE Trans Biomed Eng 56(10):2546-548. doi:10.1109/TBME.2009.2014739 CrossRef
  24. Sundnes J, Lines GT, Tveito A (2001) Efficient solution of ordinary differential equations modeling electrical activity in cardiac cells. Math Biosci 172(2):55-2. doi:10.1016/S0025-5564(01)00069-4
  25. Szafaryn LG, Skadron K, Saucerman JJ (2009) Experiences accelerating matlab systems biology applications. In: Proceedings of the workshop on biomedicine in computing: systems, architectures, and circuits (BiC), in conjunction with the 36th IEEE/ACM international symposium on computer architecture (ISCA)
  26. Tung L (1978) A bi-domain model for describing ischemic myocardial d-c potentials. Ph.D. thesis, MIT, Cambridge, Mass
  27. ten Tusscher KHWJ, Panfilov AV (2006) Alternans and spiral breakup in a human ventricular tissue model. Am J Physiol Heart Circ Physiol 291(3):H1088-100 CrossRef
  28. Vigmond EJ, Hughes M, Plank G, Leon LJ (2003) Computational tools for modeling electrical activity in cardiac tissue. J Electrocardiol 36:69-4 CrossRef
  29. W3C: Document object model (DOM) (2012). http://www.w3.org/DOM/
  
• 作者单位：Ricardo Silva Campos (1)
  Fernando Otaviano Campos (1) (2)
  Johnny Moreira Gomes (1)
  Ciro de Barros Barbosa (1)
  Marcelo Lobosco (1)
  Rodrigo Weber dos Santos (1)
  
  1. Universidade Federal de Juiz de Fora, Juiz de Fora, MG, Brazil
  2. Institute of Biophysics, Medical University of Graz, Graz, Austria
  
• ISSN：1436-5057

文摘

In silico experiments have been used for a better understanding of the electrical activity of cardiac myocytes, usually via models based on nonlinear systems of ordinary differential equations. Many different models for cardiac myocytes are available that vary on the level of complexity, depending on how detailed the phenomena is described. Long simulations of realistic and complex models are computationally expensive. To cope with this problem, this work compares different techniques to automatically speed up the numerical solution of cardiac models: (a) adaptive time step method, (b) Partial Evaluation (PE) and Lookup Tables (LUTs), and (c) an automatic way to find and exploit code concurrency via OpenMP directives. All the techniques were implemented as part of an automatic code generator for the numerical solution of models that are described in the CellML markup language. Experimental results demonstrated that the adaptive time step simulations were up to 32 times faster than the traditional Euler that use fixed time step. Combined with parallel computing on a multicore processor the execution time was further decreased and simulations were 41 times faster. Finally, the LUTs and PE techniques resulted in a 117-fold improvement in computation time over the Euler method and 72-fold improvement when compared to the traditional Rush–Larsen method.