Abstract
One of the grand challenges for computational neuroscience and high-performance computing is computer simulation of a human-scale whole brain model with spiking neurons and synaptic plasticity using supercomputers. To achieve such a simulation, the target network model must be partitioned onto a number of computational nodes, and the sub-network models are executed in parallel while communicating spike information across different nodes. However, it remains unclear how the target network model should be partitioned for efficient computing on next generation of supercomputers. Specifically, reducing the communication of spike information across compute nodes is essential, because of the relatively slower network performance than processor and memory. From the viewpoint of biological features, the cerebral cortex and cerebellum contain 99% of neurons and synapses and form layered sheet structures. Therefore, an efficient method to split the network should exploit the layered sheet structures. In this study, we indicate that a tile partitioning method leads to efficient communication. To demonstrate it, a simulation software called MONET (Millefeuille-like Organization NEural neTwork simulator) that partitions a network model as described above was developed. The MONET simulator was implemented on the Japanese flagship supercomputer K, which is composed of 82,944 computational nodes. We examined a performance of calculation, communication and memory consumption in the tile partitioning method for a cortical model with realistic anatomical and physiological parameters. The result showed that the tile partitioning method drastically reduced communication data amount by replacing network communication with DRAM access and sharing the communication data with neighboring neurons. We confirmed the scalability and efficiency of the tile partitioning method on up to 63,504 compute nodes of the K computer for the cortical model. In the companion paper by Yamaura et al., the performance for a cerebellar model was examined. These results suggest that the tile partitioning method will have advantage for a human-scale whole-brain simulation on exascale computers.
Highlights
The human brain consists of about ten to the 11th power of neurons and ten to the fifteenth power of synapses (HerculanoHouzel, 2009)
The other remaining parts of the brain are undoubtedly crucial in information processing, irrespective of the numbers of neurons and volume, we focus on the simulation of the cortex and cerebellum to appreciate the efficiency of parallel computing in the current study
In total connections per compute node, 47.4% (170 million) of the connections contained both presynaptic and postsynaptic neurons in the same tile. These results suggest that modern supercomputers consisting of compute nodes with more than 10 GB memory can benefit from the tile-partition method by replacing slow network communication with fast memory access
Summary
The human brain consists of about ten to the 11th power of neurons and ten to the fifteenth power of synapses (HerculanoHouzel, 2009). Dozens to hundreds of functional regions exist, and the types of neurons and synapses and the number of connections per neurons differ across regions To identify these quantities, large-scale measurement techniques have been suggested, including optogenetics (Deisseroth, 2015), connectome analysis (Hunnicutt et al, 2014; Oh et al, 2014; Zingg et al, 2014; Glasser et al, 2016), functional magnetic resonance imaging (Buckner et al, 2011; Yeo et al, 2011), and electroencephalograms (Parvizi and Kastner, 2018; Pesaran et al, 2018). This computational power will allow us to simulate a human-scale brain model with realistic anatomical and physiological parameters
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