Using parallel GPU architecture for simulation of planar I/F networks

Tiesel, Jan-Phillip; Maida, Anthony S.

doi:10.1109/ijcnn.2009.5178688

Cited by 8 publications

(3 citation statements)

References 8 publications

(19 reference statements)

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“…Several authors have explored the execution of SNN simulations on GPUs independently of the established simulators (e.g., [7,44]). These works share the approach of manual development and optimization of GPU code.…”

Section: Snn Simulatorsmentioning

confidence: 99%

Transitioning Spiking Neural Network Simulators to Heterogeneous Hardware

Nguyen

Andelfinger

Tan

et al. 2021

ACM Trans. Model. Comput. Simul.

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Spiking neural networks (SNN) are among the most computationally intensive types of simulation models, with node counts on the order of up to 10 11 . Currently, there is intensive research into hardware platforms suitable to support large-scale SNN simulations, whereas several of the most widely used simulators still rely purely on the execution on CPUs. Enabling the execution of these established simulators on heterogeneous hardware allows new studies to exploit the many-core hardware prevalent in modern supercomputing environments, while still being able to reproduce and compare with results from a vast body of existing literature. In this article, we propose a transition approach for CPU-based SNN simulators to enable the execution on heterogeneous hardware (e.g., CPUs, GPUs, and FPGAs), with only limited modifications to an existing simulator code base and without changes to model code. Our approach relies on manual porting of a small number of core simulator functionalities as found in common SNN simulators, whereas the unmodified model code is analyzed and transformed automatically. We apply our approach to the well-known simulator NEST and make a version executable on heterogeneous hardware available to the community. Our measurements show that at full utilization, a single GPU achieves the performance of about 9 CPU cores. A CPU-GPU co-execution with load balancing is also demonstrated, which shows better performance compared to CPU-only or GPU-only execution. Finally, an analytical performance model is proposed to heuristically determine the optimal parameters to execute the heterogeneous NEST.

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Section: Snn Simulatorsmentioning

confidence: 99%

Transitioning Spiking Neural Network Simulators to Heterogeneous Hardware

Nguyen

Andelfinger

Tan

et al. 2021

ACM Trans. Model. Comput. Simul.

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show abstract

“…These enhancements, in combination with parallel computing (Bower and Beeman, 1998; Migliore et al, 2006), have become a necessity to cope with the higher computational and the communication demands of neuroapplications. Recently, a number of developers have investigated the possibility of simulating spiking neural networks on a single Graphical Processing Unit (GPU) (Bernhard and Keriven, 2005; Fernandez et al, 2008; Fidjeland et al, 2009; Nageswaran et al, 2009a,b; Tiesel and Maida, 2009; Bhuiyan et al, 2010; Fidjeland and Shanahan, 2010; Han and Taha, 2010a,b; Hoffmann et al, 2010; Mutch et al, 2010; Scorciono, 2010; Yudanov et al, 2010; Nowotny, 2011; Ahmadi and Soleimani, 2011; Igarashi et al, 2011; Thibeault et al, 2011; Wang et al, 2011) or on multiple Graphics Processing Units (GPUs) (Brette and Goodman, 2012b). All these current simulators have shown significant improvements over their CPU only counterparts by integrating the utilization of GPUs.…”

Section: Introductionmentioning

confidence: 99%

A novel CPU/GPU simulation environment for large-scale biologically realistic neural modeling

Hoang¹,

Tanna²,

Bray³

et al. 2013

Front. Neuroinform.

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Computational Neuroscience is an emerging field that provides unique opportunities to study complex brain structures through realistic neural simulations. However, as biological details are added to models, the execution time for the simulation becomes longer. Graphics Processing Units (GPUs) are now being utilized to accelerate simulations due to their ability to perform computations in parallel. As such, they have shown significant improvement in execution time compared to Central Processing Units (CPUs). Most neural simulators utilize either multiple CPUs or a single GPU for better performance, but still show limitations in execution time when biological details are not sacrificed. Therefore, we present a novel CPU/GPU simulation environment for large-scale biological networks, the NeoCortical Simulator version 6 (NCS6). NCS6 is a free, open-source, parallelizable, and scalable simulator, designed to run on clusters of multiple machines, potentially with high performance computing devices in each of them. It has built-in leaky-integrate-and-fire (LIF) and Izhikevich (IZH) neuron models, but users also have the capability to design their own plug-in interface for different neuron types as desired. NCS6 is currently able to simulate one million cells and 100 million synapses in quasi real time by distributing data across eight machines with each having two video cards.

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“…Tiesel et al [8] reported an OpenGL implementation of a planar integrate-and-fire SNN with nearest neighbor connectivity. The forth order Runge-Kutta (RK4) integration method in a synchronous system is used.…”

Section: Introductionmentioning

confidence: 99%

GPU-based simulation of spiking neural networks with real-time performance & high accuracy

Yudanov

Shaaban

Melton

et al. 2010

The 2010 International Joint Conference on Neural Networks (IJCNN)

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A novel GPU-based simulation of spiking neural networks is implemented as a hybrid system using ParkerSochacki numerical integration method with adaptive order. Full single-precision floating-point accuracy for all model variables is achieved. The implementation is validated with exact matching of all neuron potential traces from GPU-based simulation versus those of a reference CPU-based simulation. A network of 4096 Izhikevich neurons simulated on an NVIDIA GTX260 device achieves real-time performance with a speedup of 9 compared to simulation executed on Opteron 285, 2.6-GHz device.Index Terms-GPU, CUDA, STDP, spiking neural network, high accuracy, parallel computing, shared memory.

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Using parallel GPU architecture for simulation of planar I/F networks

Cited by 8 publications

References 8 publications

Transitioning Spiking Neural Network Simulators to Heterogeneous Hardware

Transitioning Spiking Neural Network Simulators to Heterogeneous Hardware

A novel CPU/GPU simulation environment for large-scale biologically realistic neural modeling

GPU-based simulation of spiking neural networks with real-time performance & high accuracy

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Using parallel GPU architecture for simulation of planar I/F networks

Cited by 8 publications

References 8 publications

Transitioning Spiking Neural Network Simulators to Heterogeneous Hardware

Transitioning Spiking Neural Network Simulators to Heterogeneous Hardware

A novel CPU/GPU simulation environment for large-scale biologically realistic neural modeling

GPU-based simulation of spiking neural networks with real-time performance &amp; high accuracy

Contact Info

Product

Resources

About

GPU-based simulation of spiking neural networks with real-time performance & high accuracy