publication . Conference object . 2019

GPU Implementation of Neural-Network Simulations Based on Adaptive-Exponential Models

Alexandros Neofytou; George Chatzikonstantis; Ioannis Magkanaris; George Smaragdos; Christos Strydis; Dimitrios Soudris;
Open Access English
  • Published: 26 Dec 2019
  • Publisher: IEEE
Abstract
Detailed brain modeling has been presenting significant challenges to the world of high-performance computing (HPC), posing computational problems that can benefit from modern hardware-acceleration technologies. We explore the capacity of GPUs for simulating large-scale neuronal networks based on the Adaptive Exponential neuron-model, which is widely used in the neuroscientific community. Our GPU-powered simulator acts as a benchmark to evaluate the strengths and limitations of modern GPUs, as well as to explore their scaling properties when simulating large neural networks. This work presents an optimized GPU implementation that outperforms a reference multicor...
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Subjects
free text keywords: Multi-core processor, Exponential models, Computer science, Parallel computing, Exponential function, Artificial neural network, Speedup, Scaling, Computational neuroscience, Computational problem
Funded by
EC| EXA2PRO
Project
EXA2PRO
Enhancing Programmability and boosting Performance Portability for Exascale Computing Systems
  • Funder: European Commission (EC)
  • Project Code: 801015
  • Funding stream: H2020 | RIA
Validated by funder
Communities
FET H2020FET HPC: Transition to Exascale Computing
FET H2020FET HPC: Enhancing Programmability and boosting Performance Portability for Exascale Computing Systems
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