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

Georgios Smaragdos, Georgios Chatzikonstantis, Rahul Kukreja, Harry Sidiropoulos, Dimitrios Rodopoulos, Ioannis Sourdis, Zaid Al-Ars, Christoforos Kachris, Dimitrios Soudris, C.I. De Zeeuw, Christos Strydis

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Abstract

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 modeling
 field does not permit for a homogeneous acceleration platform to effectively
 address the complete array of modeling requirements.

APPROACH: In this paper
 we propose and build BrainFrame, a heterogeneous acceleration platform that
 incorporates three distinct acceleration technologies, an Intel Xeon-Phi CPU,
 a NVidia GP-GPU and a Maxeler Dataflow Engine. The PyNN software
 framework is also integrated into the platform. As a challenging proof of concept,
 we analyze the performance of BrainFrame on different experiment 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 densities, which can drastically affect
 the workload's performance characteristics.

MAIN RESULTS: The combined use of
 different HPC fabrics demonstrated that BrainFrame is better able to cope with
 the modeling diversity encountered in realistic experiments. 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 number of models already
 available. 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.&#13.

Original languageEnglish
Article number066008
JournalJournal of Neural Engineering
Volume14
DOIs
Publication statusPublished - 2017

Keywords

  • Journal Article

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