GPU Implementation of Neural-Network Simulations based on Adaptive-Exponential Models
| Autor: | Alexandros Neofytou, Ioannis Magkanaris, George Smaragdos, Dimitrios Soudris, Christos Strydis, George Chatzikonstantis |
|---|---|
| Přispěvatelé: | Neurosciences |
| Rok vydání: | 2019 |
| Předmět: |
0303 health sciences
Multi-core processor Computational neuroscience Speedup Artificial neural network Computer science Parallel computing Exponential function 03 medical and health sciences 0302 clinical medicine Benchmark (computing) Computational problem Scaling 030217 neurology & neurosurgery 030304 developmental biology |
| Zdroj: | Proceedings-2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019, 339-343 STARTPAGE=339;ENDPAGE=343;TITLE=Proceedings-2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019 BIBE 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) IEEE Symposium on Bioinformatics and Bioengineering (BIBE) |
| Popis: | 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 multicore implementation by 50x, whereas utilizing a dual-GPU configuration can deliver a speedup of 90x for networks of 20,000 fully interconnected AdEx neurons. |
| Databáze: | OpenAIRE |
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