Hybrid Parallel FDTD Calculation Method Based on MPI for Electrically Large Objects

Abstract

At present, the Internet of Things (IoT) has attracted more and more researchers' attention. Electromagnetic scattering calculation usually has the characteristics of large-scale calculation, high space-time complexity, and high precision requirement. For the background and objectives of complex environment, it is difficult for a single computer to achieve large-scale electromagnetic scattering calculation and to obtain corresponding large data. Therefore, we use Finite-Difference Time-Domain (FDTD) combined with Internet of Things, cloud computing, and other technologies to solve the above problems. In this paper, we focus on the FDTD method and use it to simulate electromagnetic scattering of electrically large objects. FDTD method has natural parallelism. A computing network cluster based on MPI is constructed. POSIX (Portable Operating System Interface of UNIX) multithreading technology is conducive to enhancing the computing power of multicore CPU and to realize multiprocessor multithreading hybrid parallel FDTD. For two-dimension CPU and memory resources, the Dominant Resource Fairness (DRF) algorithm is used to achieve load balancing scheduling, which guarantees the computing performance. The experimental results show that the hybrid parallel FDTD algorithm combined with load balancing scheduling can solve the problem of low computational efficiency and improve the success rate of task execution.

Improving Clairvoyant: reduction algorithm resilient to imbalanced process arrival patterns

“…Thus, we can observe a rapid progress in parallelization of currently existing compute methods and applications, especially ones requiring the large compute resources. We perceive that the improvements of HPC communication algorithms and protocols, including our works, will enable faster optimization of the above crucial areas (IoT, AI, Big Data), especially in such topics like parallelization of hybrid parallel FTDT methods [22], intelligent home systems supported by neural networks [26], Big Data related programming models and systems [1], and voice evaluation mechanisms involving such complex mechanisms like bio-inspired algorithms or spiking neural network [14].…”