Constructing Neuronal Network Models in Massively Parallel Environments

Frontiers in Neuroinformatics

Hans E. Plesser¹  Markus Diesmann²  Tammo Ippen⁴  Jochen M. Eppler⁶ 

[1] Department of Biosciences, Centre for Integrative Neuroplasticity, University of OsloOslo, Norway;Department of Physics, Faculty 1, RWTH Aachen UniversityAachen, Germany;Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen UniversityAachen, Germany;Faculty of Science and Technology, Norwegian University of Life SciencesÅs, Norway;Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research CentreJülich, Germany;Simulation Laboratory Neuroscience—Bernstein Facility Simulation and Database Technology, Institute for Advanced Simulation, Jülich Research Centre and JARAJülich, Germany;

关键词: multi-threading;    multi-core processor;    memory allocation;    supercomputer;    large-scale simulation;    parallel computing;   

DOI  :  10.3389/fninf.2017.00030

来源: DOAJ

【 摘 要 】

Recent advances in the development of data structures to represent spiking neuron network models enable us to exploit the complete memory of petascale computers for a single brain-scale network simulation. In this work, we investigate how well we can exploit the computing power of such supercomputers for the creation of neuronal networks. Using an established benchmark, we divide the runtime of simulation code into the phase of network construction and the phase during which the dynamical state is advanced in time. We find that on multi-core compute nodes network creation scales well with process-parallel code but exhibits a prohibitively large memory consumption. Thread-parallel network creation, in contrast, exhibits speedup only up to a small number of threads but has little overhead in terms of memory. We further observe that the algorithms creating instances of model neurons and their connections scale well for networks of ten thousand neurons, but do not show the same speedup for networks of millions of neurons. Our work uncovers that the lack of scaling of thread-parallel network creation is due to inadequate memory allocation strategies and demonstrates that thread-optimized memory allocators recover excellent scaling. An analysis of the loop order used for network construction reveals that more complex tests on the locality of operations significantly improve scaling and reduce runtime by allowing construction algorithms to step through large networks more efficiently than in existing code. The combination of these techniques increases performance by an order of magnitude and harnesses the increasingly parallel compute power of the compute nodes in high-performance clusters and supercomputers.

【 授权许可】

Unknown