A performance analysis of computing the LU and the QR matrix decompositions on the CPU and the GPU

Dušan B. Gajić, R. Stankovic, Milos Radmanovic
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引用次数: 3

Abstract

We present an analysis of time efficiency of five different implementations of the LU and the QR decomposition of matrices performed on central processing unit (CPUs) and graphics processing units (GPUs). Three of the considered implementations, developed using the Eigen C++ library, Intel MKL, and MATLAB are executed on a multi-core CPU. The remaining two implementations are processed on a GPU and employ MATLAB's Parallel Computing Toolbox and Nvidia CUDA augmented with the cuSolver library. Computation times are compared using randomly generated single- and double-precision floating-point matrices. The experiments for the LU decomposition show that the two GPU implementations offer best performance for matrices that can fit into the GPU global memory. For larger LU decomposition problem instances, Intel MKL on the CPU is found to be the fastest approach. Furthermore, Intel MKL also proves to be the fastest method for computing QR decomposition for all considered sizes of matrices.
在CPU和GPU上计算LU和QR矩阵分解的性能分析
我们分析了五种不同的LU实现的时间效率,以及在中央处理单元(cpu)和图形处理单元(gpu)上执行的矩阵QR分解。其中三个考虑的实现是在多核CPU上执行的,它们是使用Eigen c++库、Intel MKL和MATLAB开发的。其余两个实现在GPU上处理,并使用MATLAB的并行计算工具箱和Nvidia CUDA增强的cuSolver库。使用随机生成的单精度和双精度浮点矩阵比较计算时间。对LU分解的实验表明,这两种GPU实现对于能够装入GPU全局内存的矩阵提供了最好的性能。对于较大的LU分解问题实例,发现CPU上的Intel MKL是最快的方法。此外,对于所有考虑的矩阵大小,Intel MKL也被证明是计算QR分解的最快方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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