首页> 外文期刊>IEEE Transactions on Power Delivery >Graphics-Processing-Unit-Based Acceleration of Electromagnetic Transients Simulation
【24h】

Graphics-Processing-Unit-Based Acceleration of Electromagnetic Transients Simulation

机译:基于图形处理单元的电磁暂态仿真加速

获取原文
获取原文并翻译 | 示例
           

摘要

This paper presents a novel approach to speed up electromagnetic-transients (EMT) simulation, using graphics-processing-unit (GPU)-based computing. This paper extends earlier published works in the area, by exploiting additional parallelism inside EMT simulation. A 2D-parallel matrix-vector multiplication is used that is faster than previous 1D-methods. Also, this paper implements a GPU-specific sparsity technique to further speed up the simulations, as the available CPU-based sparsity techniques are not suitable for GPUs. In addition, as an extension to previous works, this paper demonstrates modelling a power-electronic subsystem. The efficacy of the approach is demonstrated using two different scalable test systems. A low granularity system, that is, one with a large cluster of buses connected to others with a few transmission lines is considered, as is also a high granularity where a small cluster of buses is connected to other clusters, thereby requiring more interconnecting transmission lines. Computation times for GPU-based computing are compared with the computation times for sequential implementations on the CPU. This paper shows two surprising differences of GPU simulation in comparison with CPU simulation. First, the inclusion of sparsity only makes minor reductions in the GPU-based simulation time. Second, excessive granularity, even though it appears to increase the number of parallel-computable subsystems, significantly slows down the GPU-based simulation.
机译:本文提出了一种基于图形处理单元(GPU)的加速电磁瞬态(EMT)仿真的新颖方法。本文通过利用EMT仿真内部的其他并行性,扩展了该领域较早发表的著作。使用的2D并行矩阵矢量乘法比以前的1D方法快。另外,由于可用的基于CPU的稀疏性技术不适用于GPU,因此本文还实现了GPU专用的稀疏性技术以进一步加快仿真速度。另外,作为对先前工作的扩展,本文演示了对电力电子子系统进行建模的方法。使用两个不同的可扩展测试系统证明了该方法的有效性。考虑了低粒度系统,即一个总线大群集通过一条传输线连接到其他总线的系统,也考虑了高粒度系统,其中总线小群集与其他群集连接的总线,因此需要更多互连的传输线。将基于GPU的计算的计算时间与CPU上顺序执行的计算时间进行比较。本文展示了与CPU仿真相比GPU仿真的两个令人惊讶的差异。首先,包含稀疏性仅会减少基于GPU的仿真时间。其次,尽管粒度过大,即使看起来增加了可并行计算的子系统的数量,但也会大大降低基于GPU的仿真速度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号