Title: A performance analysis of computing the LU and the QR matrix decompositions on the CPU and the GPU
Authors: Dušan B. Gajić; Radomir S. Stanković; Miloš Radmanović
Addresses: Department of Computing and Control Engineering, Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia ' Department of Computer Science, Faculty of Electronic Engineering, University of Niš, Niš, Serbia ' Department of Computer Science, Faculty of Electronic Engineering, University of Niš, Niš, Serbia
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.
Keywords: performance comparison; LU decomposition; QR decomposition; parallel computing; general-purpose algorithms on graphics processing unit; GPGPU; MATLAB; Intel MKL; Compute Unified Device Architecture; CUDA.
International Journal of Reasoning-based Intelligent Systems, 2017 Vol.9 No.2, pp.114 - 121
Available online: 08 Dec 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article