Gadget3 is nowadays one of the most frequently used high performing parallel codes for cosmological hydrodynamical simulations. Recent analyses have shown that the Neighbour Search process of Gadget3 is one of the most time-consuming parts. Thus, a considerable speedup can be expected from improvements of the underlying algorithms.
In this work we propose a novel approach for speeding up the Neighbour Search which takes advantage of the space-filling-curve particle ordering. Instead of performing Neighbour Search for all particles individually, nearby active particles can be grouped and one single Neighbour Search can be performed to obtain a common superset of neighbours.
Thus, with this approach we reduce the number of searches. On the other hand, tree walks are performed within a larger searching radius. There is an optimal size of grouping that maximize the speedup, which we found by numerical experiments.
We tested the algorithm within the boxes of the Magneticum large scale simulation project. As a result we obtained a speedup of 1.65 in the Density and of 1.30 in the Hydrodynamics computation, respectively, and a total speedup of 1.34.
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