发表机构
Sun Yat-sen University; National Supercomputing Center in Shenzhen（中山大学; 国家超级计算深圳中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CerebroSim在LineShine超级计算机上实现860亿神经元和100万亿突触的全脑模拟，通过DSB、RSDC和3SC技术达到24.44 PFlop/s性能，为脑疾病机制研究提供实用平台。
AI 中文摘要
构建可执行的脑模型对于推动神经科学从描述性研究走向机制性研究和预测至关重要。人类大脑规模的脉冲模拟受到高度不规则通信、多线程脉冲传递以及稀疏连接的内存成本等因素的制约。我们提出了CerebroSim，一个可扩展的全脑模拟框架。CerebroSim结合了延迟感知脉冲广播（DSB）用于聚合的延迟感知通信，无竞争突触动力学计算（RSDC）用于无锁/无原子的多线程传递并采用HBM感知优化，以及稀疏突触存储压缩（3SC）用于紧凑索引和确定性突触再生。利用从磁共振成像和弥散加权成像导出的模型，CerebroSim在LineShine超级计算机的18,432个节点、1120万个核心上模拟了860亿个神经元和100万亿个突触，持续性能达到24.44 PFlop/s，弱扩展效率为91%，强扩展效率为94%。这一能力使得受生物学约束的人类大脑模型在脑疾病的机制研究和干预假设的受控计算机模拟测试中变得切实可行。
英文摘要
Building executable brain models is essential for moving neuroscience from description to mechanism and prediction. Human-brain-scale spiking simulation is constrained by highly irregular communication, multithreaded spike delivery, and the memory cost of sparse connectivity. We present CerebroSim, a scalable framework for whole-brain simulation. CerebroSim combines Delay-aware Spike Broadcast (DSB) for aggregated delay-aware communication, Race-free Synaptic Dynamics Computation (RSDC) for lock/atomic-free multithreaded delivery with HBM-aware optimization, and Sparse Synapse Storage Compression (3SC) for compact indexing with deterministic synapse regeneration. Using a model derived from magnetic resonance imaging and diffusion-weighted imaging, CerebroSim simulates 86 billion neurons and 100 trillion synapses on 18,432 nodes across 11.2 million cores of the LineShine Supercomputer, sustaining 24.44 PFlop/s, 91% weak-scaling efficiency, and 94% strong-scaling efficiency. This capability makes biologically constrained human-brain models practical for mechanistic studies of brain disorders and controlled in silico testing of intervention hypotheses.
