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

Roy Ben-Shalom, Amit Aviv, Benjamin Razon, Alon Korngreen

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)183-194
Number of pages12
JournalJournal of Neuroscience Methods
Volume206
Issue number2
DOIs
StatePublished - 15 May 2012

Keywords

  • CUDA
  • Data fitting
  • GPGPU
  • GPU
  • Genetic algorithm
  • Graphic card
  • Neuron
  • Parallel computation
  • Voltage-gated channels

All Science Journal Classification (ASJC) codes

  • General Neuroscience

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