面向卷积完美匹配边界层的多GPU三维FDTD通信策略选择
Communication Strategy Selection for Multi-GPU 3D FDTD with Convolutional Perfectly Matched Boundary Layers
AI总结：
针对带CPML边界条件的多GPU三维FDTD计算，研究直接GPU间对等交换相比主机中转的加速效果，并评估扩大鬼域区域的影响。
摘要：
本文描述了一项针对使用CUDA进行卷积完美匹配层边界条件的三维时域有限差分计算的多GPU通信策略研究。用于确定最有效实现的指标包括运行时间、每秒百万输出点的吞吐量、强扩展效率、CPML开销、主机中转与直接GPU间对等交换的加速比，以及扩大鬼域区域的加速比。在单个NVIDIA Quadro RTX 6000 GPU上，CPML实现维持每秒2,889–3,290百万输出点，边界层开销小于1%，为多GPU研究提供了单GPU基线。结果表明，直接GPU间对等交换是主导优化，相比主机中转交换实现了2.46–2.76倍的加速，而扩大鬼域区域仅带来适度收益，因为通信频率降低部分被冗余计算和额外内存流量抵消。在NVIDIA Quadro RTX 8000 GPU上，对于测试的强扩展情况，该实现在两个GPU上提供了高达1.51倍的加速，而四个GPU能够处理接近或超过单GPU内存容量的大网格。
英文摘要：
In this paper we describe a communication-strategy study for multi-GPU three-dimensional finite-difference time-domain computation with convolutional perfectly matched layer boundary conditions using CUDA. The metrics used to determine the most effective implementation include runtime, throughput in millions of output points per second, strong-scaling efficiency, CPML overhead, host-staged versus direct GPU-to-GPU exchange speedup, and enlarged-ghost speedup. On a single NVIDIA Quadro RTX 6000 GPU, the CPML implementation sustains 2,889--3,290 million output points per second with less than 1\% boundary-layer overhead, providing the single-GPU baseline for the multi-GPU study. The results show that direct GPU-to-GPU peer exchange is the dominant optimization with a 2.46--2.76$\times$ speedup over host-staged exchange, while enlarged ghost regions give only modest benefits because the reduced communication frequency is partly offset by redundant computation and additional memory traffic. On NVIDIA Quadro RTX 8000 GPUs, the implementation gives up to a 1.51$\times$ speedup on two GPUs for the tested strong-scaling cases, while four GPUs enable larger grids that approach or exceed single-GPU memory capacity.
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