The detailed functioning of the human brain remains incompletely understood. Large-scale brain simulations complement experimental research but face substantial computational challenges: the human brain comprises approximately $10^{11}$ neurons connected by $10^{14}$ synapses, collectively forming the connectome. Empirical evidence indicates that modifications of the connectome -- specifically the formation and elimination of synapses, referred to as structural plasticity -- are essential for processes such as learning and memory formation. Connectivity updates can be computed efficiently using a Barnes--Hut-inspired approximation that reduces computational complexity from $O(n^2)$ to $O(n \log n)$, where $n$ denotes the number of neurons. Despite this improvement, communication overhead still limits scalability. Synapse updates rely heavily on remote memory access (RMA), and spike transmission requires all-to-all communication at every simulation time step. We introduce a novel algorithm that reduces communication by migrating computation rather than data. This approach reduces connectivity update time by a factor of 6 and spike transmission time by more than 2 orders of magnitude.

翻译：人类大脑的详细运作机制仍未完全阐明。大规模脑模拟实验虽能补充实验研究，但面临巨大的计算挑战：人脑约含$10^{11}$个神经元，通过$10^{14}$个突触相互连接，共同构成连接组。实验证据表明，连接组的修改——特别是突触的形成与消除，即结构可塑性——对学习与记忆形成等过程至关重要。突触连接的更新可利用受Barnes-Hut启发的近似方法高效计算，该方法将计算复杂度从$O(n^2)$降至$O(n \log n)$，其中$n$为神经元数量。尽管有此改进，通信开销仍制约可扩展性。突触更新高度依赖远程内存访问（RMA），且突触脉冲传输需要在每个模拟时间步进行全互联通信。我们提出一种新型算法，通过迁移计算而非数据来减少通信量。该算法将突触连接更新时间缩减6倍，并将突触脉冲传输时间降低超过两个数量级。