  We introduce CORTEX, an algorithmic framework designed for large-scale brain
simulation. Leveraging the computational capacity of the Fugaku Supercomputer,
CORTEX maximizes available problem size and processing performance. Our primary
innovation, Indegree Sub-Graph Decomposition, along with a suite of parallel
algorithms, facilitates efficient domain decomposition by segmenting the global
graph structure into smaller, identically structured sub-graphs. This
segmentation allows for parallel processing of synaptic interactions without
inter-process dependencies, effectively eliminating data racing at the thread
level without necessitating mutexes or atomic operations. Additionally, this
strategy enhances the overlap of communication and computation. Benchmark tests
conducted on spiking neural networks, characterized by biological parameters,
have demonstrated significant enhancements in both problem size and simulation
performance, surpassing the capabilities of the current leading open-source
solution, the NEST Simulator. Our work offers a powerful new tool for the field
of neuromorphic computing and understanding brain function.
