Matrix inversion on CPU–GPU platforms with applications in control theory

Abstract

In this paper, we tackle the inversion of large-scale dense matrices via conventional matrix factorizations (LU, Cholesky, and LDLT) and the Gauss–Jordan method on hybrid platforms consisting of a multicore CPU and a many-core graphics processor (GPU). Specifically, we introduce the different matrix inversion algorithms by using a unified framework based on the notation from the FLAME project; we develop hybrid implementations for those matrix operations underlying the algorithms, alternative to those in existing libraries for single GPU systems; and we perform an extensive experimental study on a platform equipped with state-of-the-art general-purpose architectures from Intel (Santa Clara, CA, USA) and a ‘Fermi’ GPU from NVIDIA (Santa Clara, CA, USA) that exposes the efficiency of the different inversion approaches. Our study and experimental results show the simplicity and performance advantage of the Gauss–Jordan elimination-based inversion methods and the difficulties associated with the symmetric indefinite case.