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.

In recent years, the number of artificial objects in Earth orbit has increased rapidly due to lower launch costs and new applications for satellites. More and more governments and private companies are discovering space for their own purposes. Private companies are using space as a new business field, launching thousands of satellites into orbit to offer services like worldwide Internet access. Consequently, the probability of collisions and, thus, the degradation of the orbital environment is rapidly increasing. To avoid devastating collisions at an early stage, efficient algorithms are required to identify satellites approaching each other. Traditional deterministic filter-based conjunction detection algorithms compare each satellite to every other satellite and pass them through a chain of orbital filters. Unfortunately, this leads to a runtime complexity of O(n2). In this paper, we propose two alternative approaches that rely on spatial data structures and thus allow us to exploit modern hardware’s parallelism efficiently. Firstly, we introduce a purely grid-based variant that relies on non-blocking atomic hash maps to identify conjunctions. Secondly, we present a hybrid method that combines this approach with traditional filter chains. Both implementations make it possible to identify conjunctions in a large population with millions of satellites with high precision in a comparatively short time. While the grid-based variant is characterized by lower memory consumption, the hybrid variant is faster if enough memory is available.