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Objective: The advent of High-Performance Computing (HPC) in recent years hasled to its increasing use in brain study through computational models. Thescale and complexity of such models are constantly increasing, leading tochallenging computational requirements. Even though modern HPC platforms canoften deal with such challenges, the vast diversity of the modeling field doesnot permit for a single acceleration (or homogeneous) platform to effectivelyaddress the complete array of modeling requirements. Approach: In this paper wepropose and build BrainFrame, a heterogeneous acceleration platform,incorporating three distinct acceleration technologies, a Dataflow Engine, aXeon Phi and a GP-GPU. The PyNN framework is also integrated into the platform.As a challenging proof of concept, we analyze the performance of BrainFrame ondifferent instances of a state-of-the-art neuron model, modeling the Inferior-Olivary Nucleus using a biophysically-meaningful, extended Hodgkin-Huxleyrepresentation. The model instances take into account not only the neuronal-network dimensions but also different network-connectivity circumstances thatcan drastically change application workload characteristics. Main results: Thesynthetic approach of three HPC technologies demonstrated that BrainFrame isbetter able to cope with the modeling diversity encountered. Our performanceanalysis shows clearly that the model directly affect performance and all threetechnologies are required to cope with all the model use cases.

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