Accelerating complex brain-model simulations on GPU platforms
Laboratory of Computer Engineering, Faculty of EE, Mathematics and CS, Delft University of Technology, Delft, The Netherlands
18th Design, Automation & Test in Europe conference, 2015
@inproceedings{Nguyen2015Accelerating,
author={H.A. Du Nguyen and Z. Al-Ars},
title={Accelerating complex brain-model simulations on GPU platforms},
booktitle={Proc. 18th Design, Automation & Test in Europe conference},
address={Grenoble, France},
month={March},
year={2015}
}
The Inferior Olive (IO) in the brain, in conjunction with the cerebellum, is responsible for crucial sensorimotor-integration functions in humans. In this paper, we simulate a computationally challenging IO neuron model consisting of three compartments per neuron in a network arrangement on GPU platforms. Several GPU platforms of the two latest NVIDIA GPU architectures (Fermi, Kepler) have been used to simulate large-scale IO-neuron networks. These networks have been ported on 4 diverse GPU platforms and implementation has been optimized, scoring 3x speedups compared to its unoptimized version. The effect of GPU L1-cache and thread block size as well as the impact of numerical precision of the application on performance have been evaluated and best configurations have been chosen. In effect, a maximum speedup of 160x has been achieved with respect to a reference CPU platform.
March 30, 2015 by hgpu
