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A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations.

Hahne J, Helias M, Kunkel S, Igarashi J, Bolten M, Frommer A, Diesmann M - Front Neuroinform (2015)

Bottom Line: This approach is well-suited for simulations that employ only chemical synapses but it has so far impeded the incorporation of gap-junction models, which require instantaneous neuronal interactions.To show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy in the presence of gap junctions, we present benchmarks for workstations, clusters, and supercomputers.Finally, we discuss limitations of the novel technology.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Science, Bergische Universit├Ąt Wuppertal Wuppertal, Germany.

ABSTRACT
Contemporary simulators for networks of point and few-compartment model neurons come with a plethora of ready-to-use neuron and synapse models and support complex network topologies. Recent technological advancements have broadened the spectrum of application further to the efficient simulation of brain-scale networks on supercomputers. In distributed network simulations the amount of spike data that accrues per millisecond and process is typically low, such that a common optimization strategy is to communicate spikes at relatively long intervals, where the upper limit is given by the shortest synaptic transmission delay in the network. This approach is well-suited for simulations that employ only chemical synapses but it has so far impeded the incorporation of gap-junction models, which require instantaneous neuronal interactions. Here, we present a numerical algorithm based on a waveform-relaxation technique which allows for network simulations with gap junctions in a way that is compatible with the delayed communication strategy. Using a reference implementation in the NEST simulator, we demonstrate that the algorithm and the required data structures can be smoothly integrated with existing code such that they complement the infrastructure for spiking connections. To show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy in the presence of gap junctions, we present benchmarks for workstations, clusters, and supercomputers. Finally, we discuss limitations of the novel technology.

No MeSH data available.


Related in: MedlinePlus

Comparison of simulation times on different systems. Simulation of the scaled version of the pair of neurons (Test case 1b) with different network sizes. Open symbols show the results for communication in every step ( = h) while filled symbols show the results for the original NEST communication scheme ( = dmin). The simulations on the workstation (green) are executed with 8 virtual processes (8 threads). The JUQUEEN simulations (blue) use 128 virtual processes (16 MPI processes a 8 threads). 500 ms of biological time are simulated with step size h = 0.05 ms.
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Figure 12: Comparison of simulation times on different systems. Simulation of the scaled version of the pair of neurons (Test case 1b) with different network sizes. Open symbols show the results for communication in every step ( = h) while filled symbols show the results for the original NEST communication scheme ( = dmin). The simulations on the workstation (green) are executed with 8 virtual processes (8 threads). The JUQUEEN simulations (blue) use 128 virtual processes (16 MPI processes a 8 threads). 500 ms of biological time are simulated with step size h = 0.05 ms.

Mentions: Figure 12 compares the run time of both communication strategies on JUQUEEN with their performance on workstations. On the workstation the h-step communication performs better due to the smaller number of iterations per interval and the fast communication through shared memory. On JUQUEEN, however, the communication in dmin steps outperforms communication in every step. As discussed in Section 3.1 the total number of communications (Equation 11) of the h-step communication strategy Ch exceeds Cdmin. Due to the latency of the communication in a system with distributed memory the original NEST communication strategy performs better on JUQUEEN despite the comparatively small number of 16 MPI processes.


A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations.

Hahne J, Helias M, Kunkel S, Igarashi J, Bolten M, Frommer A, Diesmann M - Front Neuroinform (2015)

Comparison of simulation times on different systems. Simulation of the scaled version of the pair of neurons (Test case 1b) with different network sizes. Open symbols show the results for communication in every step ( = h) while filled symbols show the results for the original NEST communication scheme ( = dmin). The simulations on the workstation (green) are executed with 8 virtual processes (8 threads). The JUQUEEN simulations (blue) use 128 virtual processes (16 MPI processes a 8 threads). 500 ms of biological time are simulated with step size h = 0.05 ms.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4563270&req=5

Figure 12: Comparison of simulation times on different systems. Simulation of the scaled version of the pair of neurons (Test case 1b) with different network sizes. Open symbols show the results for communication in every step ( = h) while filled symbols show the results for the original NEST communication scheme ( = dmin). The simulations on the workstation (green) are executed with 8 virtual processes (8 threads). The JUQUEEN simulations (blue) use 128 virtual processes (16 MPI processes a 8 threads). 500 ms of biological time are simulated with step size h = 0.05 ms.
Mentions: Figure 12 compares the run time of both communication strategies on JUQUEEN with their performance on workstations. On the workstation the h-step communication performs better due to the smaller number of iterations per interval and the fast communication through shared memory. On JUQUEEN, however, the communication in dmin steps outperforms communication in every step. As discussed in Section 3.1 the total number of communications (Equation 11) of the h-step communication strategy Ch exceeds Cdmin. Due to the latency of the communication in a system with distributed memory the original NEST communication strategy performs better on JUQUEEN despite the comparatively small number of 16 MPI processes.

Bottom Line: This approach is well-suited for simulations that employ only chemical synapses but it has so far impeded the incorporation of gap-junction models, which require instantaneous neuronal interactions.To show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy in the presence of gap junctions, we present benchmarks for workstations, clusters, and supercomputers.Finally, we discuss limitations of the novel technology.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Science, Bergische Universit├Ąt Wuppertal Wuppertal, Germany.

ABSTRACT
Contemporary simulators for networks of point and few-compartment model neurons come with a plethora of ready-to-use neuron and synapse models and support complex network topologies. Recent technological advancements have broadened the spectrum of application further to the efficient simulation of brain-scale networks on supercomputers. In distributed network simulations the amount of spike data that accrues per millisecond and process is typically low, such that a common optimization strategy is to communicate spikes at relatively long intervals, where the upper limit is given by the shortest synaptic transmission delay in the network. This approach is well-suited for simulations that employ only chemical synapses but it has so far impeded the incorporation of gap-junction models, which require instantaneous neuronal interactions. Here, we present a numerical algorithm based on a waveform-relaxation technique which allows for network simulations with gap junctions in a way that is compatible with the delayed communication strategy. Using a reference implementation in the NEST simulator, we demonstrate that the algorithm and the required data structures can be smoothly integrated with existing code such that they complement the infrastructure for spiking connections. To show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy in the presence of gap junctions, we present benchmarks for workstations, clusters, and supercomputers. Finally, we discuss limitations of the novel technology.

No MeSH data available.


Related in: MedlinePlus