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Graphics processing unit based acceleration of electromagnetic transients simulation

机译:基于图形处理单元的电磁瞬态仿真加速

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This paper presents a novel parallelization approach to speedup EMT simulation, using GPU-based computing. This paper extends earlier published works in the area, by exploiting additional parallelism to accelerate EMT simulation. A 2D-parallel matrix-vector multiplication is used that is faster than previous 1D-methods. Also this paper implements a simpler GPU-specific sparsity technique to further speed up the simulations as available CPU-based sparse techniques are not suitable for GPUs. Additionally, as an extension to previous works, this paper demonstrates modelling of a power electronic subsystem. A low granularity system, i.e. one with a large cluster of busses connected to others with a few transmission lines is considered, as is also a high granularity where a small cluster of busses 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. The paper shows two surprising differences of GPU simulation in comparison with CPU simulation. Firstly, the inclusion of sparsity only makes minor reductions in the GPU-based simulation time. Secondly excessive granularity, even though it appears to increase the number of parallel computable subsystems, significantly slows down the GPU-based simulation.
机译:本文提出了一种新颖的并行化方法,该方法使用基于GPU的计算来加速EMT仿真。本文通过利用其他并行性来加速EMT仿真,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的稀疏技术不适用于GPU,因此本文还实现了一种更简单的GPU专用稀疏性技术,以进一步加快仿真速度。此外,作为先前工作的扩展,本文演示了电力电子子系统的建模。考虑了低粒度系统,即一个总线大群集连接到具有少量传输线的其他总线的系统,也考虑了高粒度系统,其中总线小群集连接到其他群集的高粒度,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,即使它似乎增加了并行可计算子系统的数量,也显着降低了基于GPU的仿真速度。

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