QR decomposition on GPUs

QR decomposition is a computationally intensive linear algebra operation that factors a matrix A into the product of a unitary matrix Q and upper triangular matrix R. Adaptive systems commonly employ QR decomposition to solve overdetermined least squares problems. Performance of QR decomposition is typically the crucial factor limiting problem sizes. Graphics Processing Units (GPUs) are high-performance processors capable of executing hundreds of floating point operations in parallel. As commodity accelerators for 3D graphics, GPUs offer tremendous computational performance at relatively low costs. While GPUs are favorable to applications with much inherent parallelism requiring coarse-grain synchronization between processors, methods for efficiently utilizing GPUs for algorithms computing QR decomposition remain elusive. In this paper1 , we discuss the architectural characteristics of GPUs and explain how a high-performance implementation of QR decomposition may be implemented. We pr...

• Type: Conference
• Year: 2009
• Where: ASPLOS
• Authors: Andrew Kerr, Dan Campbell, Mark Richards
• Keywords: ASPLOS 2009, GPUs Offer, Programming Languages, QR Decomposition, QR Implementation