Speed comparisons with MATLAB

Solution speed is important for complex computational models and here we compare the performance of OpenCOR with MATLAB[1]. Nine representative CellML models were chosen from the PMR model repository. For the MATLAB tests we used the MATLAB code, generated automatically from CellML, that is available on the PMR site. These comparisons are based on using the default solvers (listed below) available in the two packages.

Testing environment

  • MacBook Pro (Retina, Mid 2012).
  • Processor: 2.6 GHz Intel Core i7.
  • Memory: 16 GB 1600 MHz DDR3.
  • Operating system: OS X Yosemite 10.10.3.

OpenCOR

  • Version: 0.4.1.
  • Solver: CVODE with its default settings, except for its Maximum step parameter, which is set to the model’s stimulation duration, if needed.

MATLAB

  • Version: R2013a.
  • Solver: ode15s (i.e. a solver suitable for stiff problems and which has low to medium order of accuracy) with both its RelTol and AbsTol parameters set to 1e-7 and its MaxStep parameter set to the stimulation duration, if needed.

Testing protocol

  • Run a model for a given simulation duration.
  • Generate simulation data every milliseconds.
  • Only keep track of all the simulation data (i.e. no graphical output).
  • Run a model 7 times, discard the 2 slowest runs (to account for unpredictable slowdowns of the testing machine) and average the resulting computational times.
  • Computational times are obtained directly from OpenCOR and MATLAB (through a couple of calls to cputime in the case of MATLAB).

Results

CellML model (from PMR on 18/6/2015) Duration (s) OpenCOR time (s) MATLAB time (s) Time ratio (MATLAB/OpenCOR)
Bondarenko et al. 2004 10 1.16 140.14 121
Courtemanche et al. 1998 100 0.998 45.720 46
Faber & Rudy 2000 50 0.717 29.010 40
Garny et al. 2003 100 0.996 48.180 48
Luo & Rudy 1991 200 0.666 70.070 105
Noble 1962 1000 1.42 310.02 218
Noble et al. 1998 100 0.834 42.010 50
Nygren et al. 1998 100 0.824 31.370 38
ten Tusscher & Panfilov 2006 100 0.969 59.080 61

*The value of membrane.stim_end was increased so as to get action potentials for the duration of the simulation

Conclusions

For this range of tests, OpenCOR is between 38 and 218 times faster than MATLAB. A more extensive evaluation of these results is available on GitHub[2].

Footnotes

[1]www.mathworks.com/products/matlab
[2]https://github.com/opencor/speedcomparison. These tests were carried out by Alan Garny.