FW–H 声类比广泛用于噪声预测,但大积分面和长声源时间历程会使后处理计算成本很高。本文面向配备深度计算单元(DCU,即 GPU 类加速器)的平台,提出基于 MPI 的多节点 FW–H 求解器。主机 CPU 负责数据准备、节点间通信和输入输出,主要计算量所在的 FW–H 积分卸载至 DCU。观察点、时间或频率样本及面元上的嵌套循环映射到三维线程网格,并通过分布式域分解减少各进程内存占用。典型二维和三维基准验证中,远场指向性的平均相对误差分别低于 0.15% 和 0.70%。在代表性二维频域基准中,相比经过优化的 32 核海光 C86 7185 CPU 实现,单张海光 DCU 的端到端加速最高达 21.2 倍,积分内核最高达 708 倍。该二维算例的 CPU 实现在较低至中等核数下近线性扩展,但接近 32 核时出现饱和;大型三维时域基准中,CPU 和 DCU 分别扩展至 128 核和 128 张 DCU 时仍表现良好,DCU 接近线性,部分配置达到超线性加速。分析表明,CPU 饱和主要与 MPI 开销和节点内资源竞争增加有关;DCU 超线性加速主要来自单设备任务减少后分块次数及主机—设备传输开销降低。基于额定功率的估算还表明,DCU 配置完成求解的能耗低于相应 CPU 配置。该实现为气动声学预测提供了准确、高效且可扩展的 FW–H 计算框架。
The Ffowcs Williams–Hawkings (FW–H) acoustic analogy is widely used for noise prediction, but large integration surfaces and long source-time histories can make the post-processing computationally expensive. To address this issue, we present a Message Passing Interface (MPI)-based multi-node implementation of the FW–H solver for platforms equipped with Deep Computing Units (DCUs), i.e., GPU-class accelerators. In this framework, host CPUs handle data preparation, inter-node communication, and I/O, while the computationally dominant FW–H integration is offloaded to the DCUs. The nested loops over observers, time/frequency samples, and surface elements are mapped onto a three-dimensional thread grid, and distributed domain decomposition is adopted to reduce the memory footprint on each rank. The implementation is validated on canonical two-dimensional and three-dimensional benchmarks, with mean relative errors in far-field directivity below 0.15% and 0.70%, respectively. For a representative 2D frequency-domain benchmark, a single Hygon DCU achieves an end-to-end speedup of up to 21.2 × over an optimized 32-core Hygon C86 7185 CPU implementation, and up to 708 × for the FW–H integration kernel. In this 2D case, the CPU implementation scales nearly linearly at low to moderate core counts but shows signs of saturation near 32 cores, whereas for the large 3D time-domain benchmark, both CPU and DCU implementations exhibit robust multi-node scalability up to 128 CPU cores and 128 DCUs, with the DCU path showing near-linear scaling and, in some configurations, superlinear speedup. Scalability analysis indicates that the CPU saturation trend is mainly associated with increasing MPI overhead and intra-node resource contention, whereas the superlinear DCU speedup arises primarily from reduced chunking and lower host–device transfer overhead as the per-device workload decreases. A rated-power-based estimate also suggests lower energy-to-solution for the DCU configurations than for the corresponding CPU configurations. Overall, the proposed multi-DCU implementation provides an accurate, efficient, and scalable framework for FW–H-based aeroacoustic prediction.