Improved GPU near neighbours performance for multi-agent simulations

Complex systems simulations are well suited to the SIMT paradigm of GPUs, enabling millions of actors to be processed in fractions of a second. At the core of many such simulations, fixed radius near neighbours (FRRN) search provides the actors with spatial awareness of their neighbours. The FRNN search process is frequently the limiting factor of performance, due to the disproportionate level of scattered memory reads demanded by the query stage, leading to FRNN search runtimes exceeding that of simulation logic. In this paper, we propose and evaluate two novel optimisations (Strips and Proportional Bin Width) for improving the performance of uniform spatially partitioned FRNN searches and apply them in combination to demonstrate the impact on the performance of multi-agent simulations. The two approaches aim to reduce latency in search and reduce the amount of data considered (i.e. more efficient searching), respectively. When the two optimisations are combined, the peak obtained speedups observed in a benchmark model are 1.27x and 1.34x in two and three dimensional implementations, respectively. Due to additional non FRNN search computation, the peak speedup obtained when applied to complex system simulations within FLAMEGPU is 1.21x. (C) 2019 The Authors. Published by Elsevier Inc.