TY - JOUR
T1 - BrainFrame
T2 - A node-level heterogeneous accelerator platform for neuron simulations
AU - Smaragdos, Georgios
AU - Chatzikonstantis, Georgios
AU - Kukreja, Rahul
AU - Sidiropoulos, Harry
AU - Rodopoulos, Dimitrios
AU - Sourdis, Ioannis
AU - Al-Ars, Zaid
AU - Kachris, Christoforos
AU - Soudris, Dimitrios
AU - De Zeeuw, C.I.
AU - Strydis, Christos
N1 - Creative Commons Attribution license.
PY - 2017
Y1 - 2017
N2 - 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.
.
AB - 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.
.
KW - Journal Article
U2 - 10.1088/1741-2552/aa7fc5
DO - 10.1088/1741-2552/aa7fc5
M3 - Article
C2 - 28707628
SN - 1741-2560
VL - 14
JO - Journal of Neural Engineering
JF - Journal of Neural Engineering
M1 - 066008
ER -