重新审视总线导纳矩阵:现代计算机的性能挑战

IF 3.3 Q3 ENERGY & FUELS
Hantao Cui
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引用次数: 0

摘要

总线导纳矩阵广泛应用于电力工程中的网络建模。由于其高度稀疏,在计算时需要较少的 CPU 运算。同时,稀疏矩阵计算涉及大量索引和标量操作,不利于现代处理器的使用。在不使用导纳矩阵的情况下,节点功率注入和相应的稀疏雅各布矩阵可以通过一种 "按元素计算 "的方法来计算,该方法由一个高度规则的矢量化评估步骤和一个还原步骤组成。本文通过将基于导纳矩阵的方法与按元素计算的方法进行比较,重新审视了该方法在功率注入和雅各布矩阵计算方面的计算性能。案例研究表明,对于具有数千至数十万总线的网格测试案例,尤其是在支持宽矢量指令的 CPU 上,导纳矩阵法的计算速度通常比元素法慢。本文还分析了矢量指令宽度和内存速度的影响,以预测未来计算机的发展趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bus Admittance Matrix Revisited: Performance Challenges on Modern Computers
Bus admittance matrix is widely used in power engineering for network modeling. Being highly sparse, it requires fewer CPU operations when used for calculations. Meanwhile, sparse matrix calculations involve numerous indexing and scalar operations, which are unfavorable to modern processors. Without using the admittance matrix, nodal power injections and the corresponding sparse Jacobian can be computed by an element-wise method, which consists of a highly regular, vectorized evaluation step and a reduction step. This paper revisits the computational performance of the admittance matrix-based method, in terms of power injection and Jacobian matrix calculation, by comparing it with the element-wise method. Case studies show that the admittance matrix method is generally slower than the element-wise method for grid test cases with thousands to hundreds of thousands of buses, especially on CPUs with support for wide vector instructions. This paper also analyzes the impact of the width of vector instructions and memory speed to predict the trend for future computers.
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来源期刊
CiteScore
7.80
自引率
5.30%
发文量
45
审稿时长
10 weeks
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