Mathematical models with varying degrees of complexity have been proposed and simulated in an attempt to represent the intricate mechanisms of the human neuron. One of the most biochemically realistic and analytical models, based on the Hodgkin–Huxley (HH) model, has been selected for study in this paper. In order to satisfy the model's computational demands, we present a simulator implemented on Intel Xeon Phi Knights Landing manycore processors. This high-performance platform features an x86-based architecture, allowing our implementation to be portable to other common manycore processing machines. This is reinforced by the fact that Phi adopts the popular OpenMP and MPI programming models. The simulator performance is evaluated when calculating neuronal networks of varying sizes, density and network connectivity maps. The evaluation leads to an analysis of the neuronal synaptic patterns and their impact on performance when tackling this type of workload on a multinode system. It will be shown that the simulator can calculate 100 ms of simulated brain activity for up to 2 millions of biophysically-accurate neurons and 2 billion neuronal synapses within one minute of execution time. This level of performance renders the application an efficient solution for large-scale detailed model simulation.

Computational neuroscience, Intel Xeon Phi Knights Landing, Multinode, Simulation,
This work was funded by the European Commission 7th Framework Programme; grant id h2020/687628 - Versatile Integrated Accelerator-based Heterogeneous Data Centres (VINEYARD)
Erasmus MC: University Medical Center Rotterdam

Chatzikonstantis, G, Sidiropoulos, H. (H.), Strydis, C, Negrello, M, Smaragdos, G, de Zeeuw, C.I, & Soudris, D. (2018). Multinode implementation of an extended Hodgkin–Huxley simulator. Neurocomputing. doi:10.1016/j.neucom.2018.10.062