Optimizing ion channel models using a parallel genetic algorithm on graphical processors

We have recently shown that we can semi-automatically constrain models of voltage-gated ion channels by combining a stochastic search algorithm with ionic currents measured using multiple voltage-clamp protocols. Although numerically successful, this approach is highly demanding computationally, with optimization on a high performance Linux cluster typically lasting several days. To solve this computational bottleneck we converted our optimization algorithm for work on a graphical processing unit (GPU) using NVIDIA's CUDA. Parallelizing the process on a Fermi graphic computing engine from NVIDIA increased the speed ∼180 times over an application running on an 80 node Linux cluster, considerably reducing simulation times. This application allows users to optimize models for ion channel kinetics on a single, inexpensive, desktop “super computer,” greatly reducing the time and cost of building models relevant to neuronal physiology. We also demonstrate that the point of algorithm parallelization is crucial to its performance. We substantially reduced computing time by solving the ODEs (Ordinary Differential Equations) so as to massively reduce memory transfers to and from the GPU. This approach may be applied to speed up other data intensive applications requiring iterative solutions of ODEs.

► We present an application for optimizing ion channel models using a graphic processing unit.
► Parallelizing the process on a Tesla graphic computing engine from NVIDIA increased the speed ∼180 times over an application running on a 80 node Linux cluster, reducing simulation times considerably.
► This application allows users to optimize ion channel kinetics on a single, inexpensive, desktop “super computer”.
► We demonstrate that the point at which the flow of the algorithm is parallelized is crucial to its performance.
► Our approach could be applied to speedup other data intensive applications requiring iterative solutions of differential equations.