Treffer: GPU 并行计算驱动的大规模无人机集群仿真研究.

With the continuous expansion of unmanned aerial vehicle (UAV) swarm simulation scales, traditional CPU-based serial computing methods have become increasingly inadequate in meeting the high-efficiency, real-time, and low-cost requirements of large-scale simulations. To address this challenge, this paper proposes a GPU- parallel-computing-driven method for large- scale UAV swarm simulation. From a methodological perspective, a parallel simulation framework compatible with GPU execution characteristics is systematically developed. Leveraging CUDA as the foundation for parallel computation, the method exploits the inherent independence of simulation objects by adopting a fine-grained data-parallel strategy. Simulation tasks are evenly distributed across parallel threads based on a static load-balancing principle and thread mapping model, thereby maximizing computational resource utilization. To validate the effectiveness of the proposed method, a parallel simulation system for large-scale UAV swarms is constructed, and performance benchmarking against conventional CPU-based simulation approaches is conducted. The evaluation is systematically performed from two critical dimensions: simulation real-time performance and system scalability. Experimental results demonstrate that while ensuring simulation accuracy and system stability, this method can achieve real-time simulation of up to 2 322 drone models on an NVIDIA GeForce GTX 1650 graphics card. This work provides a practical solution for efficient UAV swarm simulation and offers valuable technical insights for addressing large-scale parallel computing challenges in other domains. [ABSTRACT FROM AUTHOR]

随着无人机集群仿真规模的不断扩大, 传统基于 CPU 的串行计算方式已难以满足大规模仿真高效率、高实时性和低成本的实际需求。提出一种 GPU 并行计算驱动的大规模无人机集群仿真方法, 从方法设计层面出发, 系统 构建契合 GPU 执行特性的并行仿真机制。该方法以 CUDA 为并行计算基础, 结合仿真对象的独立性特征, 采用细 粒度的数据并行策略, 依据静态负载均衡原则与线程映射模型, 将仿真任务均匀分配至并行线程, 实现计算资源的 高效利用。为验证所提出方法的有效性, 构建面向大规模集群的并行仿真系统, 并与传统 CPU 仿真方法进行性能对 比测试, 从仿真实时性与系统扩展性两个维度深入开展性能评估。实验结果表明, 该方法在保障仿真精度和系统稳 定性的同时, 能实现在 NVIDIA GeForce GTX 1650 显卡上至多 2 322 架无人机模型的实时仿真, 不仅为无人机集群 的高效仿真提供了可行路径, 也为其他领域的大规模并行计算问题提供了技术参考。 [ABSTRACT FROM AUTHOR]

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