In recent years, applications such as real-time simulations, autonomous systems, and video games increasingly demand the processing of complex geometric models under stringent time constraints. Traditional geometric algorithms, including the convex hull, are subject to these challenges. A common approach to improve performance is scaling computational resources, which often results in higher energy consumption. Given the growing global concern regarding sustainable use of energy, this becomes a critical limitation. This work presents a 3D preprocessing filter for the convex hull algorithm using ray tracing and tensor core technologies. The filter builds a delimiter polyhedron based on Manhattan distances that discards points from the original set. The filter is evaluated on two point distributions: uniform and sphere. Experimental results show that the proposed filter, combined with convex hull construction, accelerates the computation of the 3D convex hull by up to $200 \times$ with respect to a CPU parallel implementation. This research demonstrates that geometric algorithms can be accelerated through massive parallelism while maintaining efficient energy utilization. Beyond execution time and speedup evaluation, we also analyze GPU energy consumption, showing that the proposed preprocessing filter not only reduces the computational workload but also achieves performance gains with controlled energy usage. These results highlight the dual benefit of the method in terms of both speed and energy efficiency, reinforcing its applicability in modern high-performance scenarios.

翻译：近年来，实时仿真、自主系统和视频游戏等应用对复杂几何模型的处理提出了日益严苛的时间要求。包括凸包在内的传统几何算法正面临这些挑战。提升性能的常见方法是扩展计算资源，但这往往导致更高的能耗。鉴于全球对可持续能源利用的日益关注，这已成为一个关键限制。本研究提出了一种利用光线追踪与张量核技术的凸包算法三维预处理滤波器。该滤波器基于曼哈顿距离构建边界多面体，以剔除原始点集中的冗余点。滤波器在均匀分布与球面分布两种点集上进行了评估。实验结果表明，所提出的滤波器结合凸包构建算法，相较于CPU并行实现，能将三维凸包计算速度提升高达$200 \times$。本研究表明，几何算法可通过大规模并行化实现加速，同时保持高效的能源利用。除执行时间和加速比评估外，我们还分析了GPU能耗，证明所提出的预处理滤波器不仅能减少计算负载，还能在可控能耗下获得性能提升。这些结果凸显了该方法在速度与能效方面的双重优势，增强了其在现代高性能场景中的适用性。