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.

翻译：我们提出了CORTEX，一种面向大规模脑模拟的算法框架。通过利用富岳超级计算机的计算能力，CORTEX最大化了可处理的问题规模与运算性能。其核心创新在于入度子图分解技术，配合一套并行算法，通过将全局图结构分割为多个结构一致的子图，实现了高效的域分解。这种分割方式使得突触交互的并行处理无需进程间依赖，在线程层面彻底消除了数据竞争，无需互斥锁或原子操作。此外，该策略还增强了通信与计算的重叠能力。基于生物参数特征的脉冲神经网络基准测试表明，该方法在问题规模和模拟性能上均显著提升，超越了当前领先的开源解决方案NEST模拟器。本研究为神经形态计算领域及理解大脑功能提供了强大的新工具。