  Smoothed Particle Hydrodynamics (SPH) is essential for modeling complex
large-deformation problems across various applications, requiring significant
computational power. A major portion of SPH computation time is dedicated to
the Nearest Neighboring Particle Search (NNPS) process. While advanced NNPS
algorithms have been developed to enhance SPH efficiency, the potential
efficiency gains from modern computation hardware remain underexplored. This
study investigates the impact of GPU parallel architecture, low-precision
computing on GPUs, and GPU memory management on NNPS efficiency. Our approach
employs a GPU-accelerated mixed-precision SPH framework, utilizing
low-precision float-point 16 (FP16) for NNPS while maintaining high precision
for other components. To ensure FP16 accuracy in NNPS, we introduce a Relative
Coordinated-based Link List (RCLL) algorithm, storing FP16 relative coordinates
of particles within background cells. Our testing results show three
significant speedup rounds for CPU-based NNPS algorithms. The first comes from
parallel GPU computations, with up to a 1000x efficiency gain. The second is
achieved through low-precision GPU computing, where the proposed FP16-based
RCLL algorithm offers a 1.5x efficiency improvement over the FP64-based
approach on GPUs. By optimizing GPU memory bandwidth utilization, the
efficiency of the FP16 RCLL algorithm can be further boosted by 2.7x, as
demonstrated in an example with 1 million particles. Our code is released at
https://github.com/pnnl/lpNNPS4SPH.
