AI 中文总结
针对时域积分方程求解中 retarded 格林函数组装的计算瓶颈，提出因果感知交互选择策略，通过先验排除不可接受交互降低复杂度，实现交互量、算法及组装时间的显著加速。
AI 中文摘要
在时域积分方程（TDIE）求解器中， retarded 格林函数交互的组装仍是主要计算瓶颈。我们提出一种因果感知组装策略，利用 retarded 格林函数的有限时空支撑，在数值评估前识别可接受交互。以因果关系为精确交互选择准则，先验排除因果上不可接受的交互，在不修改底层 TDIE 公式或引入近似的情况下降低组装复杂度。数值验证表明，该剪枝保留了 TDIE 的瞬态响应。剩余可接受交互被组织为向量化共享内存工作负载以实现高效组装。结果显示，被评估交互减少了 41%，单线程算法加速 2.6 倍，使用 64 个 CPU 线程时总组装时间加速最高达 109 倍。
英文摘要
In time-domain integral-equation (TDIE) solvers, the assembly of the retarded Green-function interactions remains a major computational bottleneck. We present a causality-aware assembly strategy that exploits the finite space--time support of the retarded Green function to identify admissible interactions prior to numerical evaluation. By using causality as an exact interaction-selection criterion, causally inadmissible interactions are excluded a priori, reducing assembly complexity without modifying the underlying TDIE formulation or introducing approximations. Numerical validation confirms that the proposed pruning preserves the TDIE transient response. The remaining admissible interactions are organized into vectorized shared-memory workloads for efficient assembly. The results demonstrate a 41 % reduction in evaluated interactions, a 2.6 times single-worker algorithmic speedup, and a total assembly-time speedup of up to 109 times using 64 CPU workers.
Comments5 pages, 4 figures
