To fully exploit hardware resources and maximize the throughput of the heterogeneous systems, a graphics processing unit (GPU)+CPU collaborative computing method for the finite-difference time-domain (FDTD) method scattering simulation is proposed in this article. For the workload imbalance caused by different computational performances and device-module affinity of different processors in the heterogeneous systems, a dynamic load balancing method is proposed, which can distribute parallel tasks intelligently according to the captured runtime performance index of processors. The method proposed herein can be applied to different heterogeneous hardware environments adaptively, owing to its online adjustment of workload distribution. Simulation results show that the proposed dynamic load balancing-based heterogeneous parallel FDTD method does not introduce additional errors in solving the scattering problems and has a great improvement in efficiency compared to a parallel implementation using a single kind of processor. It shows up to $56.76\times $ and $5.85\times $ speedups over the multicore CPU and multi-GPU parallel execution, respectively, which is quite an attractive improvement for practical applications.

中文翻译：

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异构 CPU+GPU 加速 FDTD 解决动态负载均衡的分散问题

为了充分利用硬件资源，最大化异构系统的吞吐量，本文提出了一种用于有限差分时域（FDTD）方法散射仿真的图形处理单元（GPU）+CPU协同计算方法。针对异构系统中不同处理器的计算性能和设备模块亲和性不同导致的工作负载不平衡，提出了一种动态负载平衡方法，该方法可以根据捕获的处理器运行时性能指标智能地分配并行任务。由于其工作负载分布的在线调整，本文提出的方法可以自适应地应用于不同的异构硬件环境。仿真结果表明，所提出的基于动态负载平衡的异构并行FDTD方法在解决散射问题时不会引入额外的错误，与使用单一处理器的并行实现相比，效率有了很大的提高。它显示到 $56.76\times $ 和 $5.85\times $ 分别比多核 CPU 和多 GPU 并行执行更快，这对于实际应用来说是一个非常有吸引力的改进。