I Like To Move It -- Computation Instead of Data in the Brain
For neuroscientists and computational modelers, this work significantly improves the scalability of brain simulations by reducing communication overhead, enabling more efficient large-scale simulations.
The paper tackles the computational challenges of large-scale brain simulations, specifically the high communication overhead in structural plasticity and spike transmission. The proposed algorithm reduces connectivity update time by a factor of 6 and spike transmission time by over 100x.
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.