BrainFrame: a node-level heterogeneous accelerator platform for neuron simulations
Artikel i vetenskaplig tidskrift, 2017

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.

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Författare

G. Smaragdos

Erasmus Universiteit Rotterdam

Georgios Chatzikonstantis

National Technical University of Athens (NTUA)

Rahul Kukreja

TU Delft

Harry Sidiropoulos

National Technical University of Athens (NTUA)

Dimitrios Rodopoulos

Interuniversitair Micro-Elektronica Centrum (IMEC)

Ioannis Sourdis

Chalmers, Data- och informationsteknik, Datorteknik

Zaid Al-Ars

TU Delft

Christoforos Kachris

National Technical University of Athens (NTUA)

D. Soudris

National Technical University of Athens (NTUA)

Chris De Zeeuw

Erasmus Universiteit Rotterdam

C. Strydis

Erasmus Universiteit Rotterdam

Journal of Neural Engineering

1741-2560 (ISSN) 17412552 (eISSN)

Vol. 14 6 066008

Ämneskategorier

Annan klinisk medicin

Radiologi och bildbehandling

DOI

10.1088/1741-2552/aa7fc5

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2022-11-09