论文标题
高性能计算的特征向量组件计算速度
Eigenvector Component Calculation Speedup over NumPy for High-Performance Computing
论文作者
论文摘要
与人工智能，机器学习和系统识别模拟有关的应用基本上使用特征向量。使用常规方法计算非常大的矩阵的特征向量是计算密集型的，并且使应用程序缓慢。最近，特征向量 - 元素价值公式有望确定明显的加速。我们研究了针对现有的最新算法及其评估性能提升的实施的公式的算法实施。我们对公式的实施提供了第一个同类系统研究。我们利用使用Lapack和Blas的本机Numpy特征向量实现的高性能计算概念来展示进一步的改进。
Applications related to artificial intelligence, machine learning, and system identification simulations essentially use eigenvectors. Calculating eigenvectors for very large matrices using conventional methods is compute-intensive and renders the applications slow. Recently, Eigenvector-Eigenvalue Identity formula promising significant speedup was identified. We study the algorithmic implementation of the formula against the existing state-of-the-art algorithms and their implementations to evaluate the performance gains. We provide a first of its kind systematic study of the implementation of the formula. We demonstrate further improvements using high-performance computing concepts over native NumPy eigenvector implementation which uses LAPACK and BLAS.
