Brain modeling has been receiving significant attention over the years, both for its neuroscientific potential and for its challenges in the context of high-performance computing. The development of physiologically plausible neuron models comes at the cost of increased complexity. In this work, we have selected a highly computationally demanding model of the Inferior-Olivary Nucleus (InfOli) based on the Hodgkin-Huxley (HH) neuron model. This brain region, functionally coupled with the cerebellum, is of vital importance for motor skills and time-sensitive cognitive functions. The computing fabric of choice is an Intel Xeon/Xeon Phi system, which is a typical node of modern computing infrastructure. The target application is parallelized with various combinations of MPI and OpenMP and performance is measured on the target platform. The different implementations are compared and the best one is chosen. Further optimization of this implementation is presented in detail. Its behaviour is then examined when scaling up to neuron populations representative of realistic, human Inferior-Olivary neuronal networks. The evaluation's results highlight the importance of examining a network's size and density before choosing the best platform for its simulation. All the parallelization and vectorization options presented in the current paper are available on a public repository for further examination.

MPI, neuron modeling, OpenMP, performance, vectorization, Xeon Phi,
IEEE Transactions on Parallel and Distributed Systems
no subscription

Chatzikonstantis, G, Rodopoulos, D, Strydis, C, de Zeeuw, C.I, & Soudris, D. (2017). Optimizing extended hodgkin-huxley neuron model simulations for a Xeon/Xeon Phi Node. IEEE Transactions on Parallel and Distributed Systems, 28(9), 2581–2594. doi:10.1109/TPDS.2017.2686389