Efficient Parallel Simulation of Large-Scale Neuronal Networks on Clusters of Multiprocessor Computers
To understand the principles of information processing in the brain, we depend on models with more than 105 neurons and 109 connections. These networks can be described as graphs of threshold elements that exchange point events over their connections.
From the computer science perspective, the key challenges are to represent the connections succinctly; to transmit events and update neuron states efficiently; and to provide a comfortable user interface. We present here the neural simulation tool NEST, a neuronal network simulator which addresses all these requirements. To simulate very large networks with acceptable time and memory requirements, NEST uses a hybrid strategy, combining distributed simulation across cluster nodes (MPI) with thread-based simulation on each computer. Benchmark simulations of a computationally hard biological neuronal network model demonstrate that hybrid parallelization yields significant performance benefits on clusters of multi-core computers, compared to purely MPI-based distributed simulation.
KeywordsTarget Node Spike Time Parallel Simulation Stimulation Device Node List
Unable to display preview. Download preview PDF.
- 1.Braitenberg, V., Schüz, A.: Cortex: Statistics and Geometry of Neuronal Connectivity, 2nd edn. Springer, Berlin (1998)Google Scholar
- 2.Gewaltig, M.O., Diesmann, M.: NEST. Scholarpedia (2007)Google Scholar
- 3.Message Passing Interface Forum: MPI: A message-passing interface standard. Technical Report UT-CS-94-230, University of Tennessee (1994)Google Scholar
- 4.Lewis, B., Berg, D.J.: Multithreaded programming with pthreads. Sun Microsystems, Mountain View (1998)Google Scholar
- 5.Knuth, D.E.: The Art of Computer Programming, 3rd edn., vol. 2. Addison-Wesley, Reading, MA (1998)Google Scholar
- 7.Bi, G.Q., Poo, M.M.: Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 18, 10464–10472 (1998)Google Scholar
- 9.Zeigler, B.P., Praehofer, H., Kim, T.G.: Theory of Modeling and Simulation, 2nd edn. Academic Press, Amsterdam (2000)Google Scholar
- 10.Brette, R., et al.: Simulation of networks of spiking neurons: A review of tools and strategies. J. Comput. Neurosci. (in press, 2007)Google Scholar
- 15.Tam, A., Wang, C.: Efficient scheduling of complete exchange on clusters. In: 13th International Conference on Parallel and Distributed Computing Systems (PDCS 2000), Las Vegas (2000)Google Scholar
- 18.Woll, A.: Performance analysis of an MPI- and thread-parallel neural network simulator. Master’s thesis, Norwegian University of Life Sciences (2007)Google Scholar
- 19.Djurfeldt, M., Johansson, C., Ekeberg, Ö., Rehn, M., Lundqvist, M., Lansner, A.: Massively parallel simulation of brain-scale neuronal network models. Technical Report Technical Report TRITA-NA-P0513, KTH, School of Computer Science and Communication Stockholm, Stockholm (2005)Google Scholar