BrainFrame: A node-level heterogeneous accelerator platform for neuron simulations

Objective: The advent of High-Performance Computing (HPC) in recent years has led to its increasing use in brain study through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational requirements. Even though modern HPC platforms can often deal with such challenges, the vast diversity of the modelling field does not permit for a single acceleration (or homogeneous) platform to effectively address the complete array of modelling requirements. Approach: In this paper we propose and build BrainFrame, a heterogeneous acceleration platform, incorporating three distinct acceleration technologies, a Dataflow Engine, a Xeon Phi and a GP-GPU. The PyNN framework is also integrated into the platform. As a challenging proof of concept, we analyse the performance of BrainFrame on different instances of a state-of-the-art neuron model, representing the Inferior-Olivary Nucleus using a biophysically-meaningful, extended Hodgkin-Huxley representation. The model instances take into account not only the neuronal-network dimensions but also different network-connectivity circumstances that can drastically change HPC workload characteristics. Main results: The combined use of HPC fabrics demonstrated that BrainFrame is better able to cope with the modelling diversity encountered. Our performance analysis shows clearly that the model directly affects performance and all three technologies are required to cope with all the model use cases. Significance: The BrainFrame framework is designed to transparently configure and select the appropriate back-end accelerator technology for use per simulation run. The PyNN integration provides a familiar bridge to the vast array of modelling work already conducted. Additionally, it gives a clear roadmap for extending the platform support beyond the proof-of-concept, with improved usability and directly useful features to the computational-neuroscience community, paving the way for wider adoption