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  1. Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units (GPGPU-2)
  2. QR decomposition on GPUs
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Accelerating cosmological data analysis with graphics processors
High performance computation and interactive display of molecular orbitals on GPUs and multi-core CPUs
GPU acceleration of a production molecular docking code
Accelerating phase unwrapping and affine transformations for optical quadrature microscopy using CUDA
Performance analysis of accelerated image registration using GPGPU
Accelerating linpack with CUDA on heterogenous clusters
hiCUDA: a high-level directive-based language for GPU programming
Architecture-aware optimization targeting multithreaded stream computing
QR decomposition on GPUs
3D finite difference computation on GPUs using CUDA
Optimization of tele-immersion codes
Understanding software approaches for GPGPU reliability

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QR decomposition on GPUs

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QR decomposition on GPUs

Content Provider ACM Digital Library
Author Campbell, Dan Kerr, Andrew Richards, Mark
Abstract 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 paper, we discuss the architectural characteristics of GPUs and explain how a high-performance implementation of QR decomposition may be implemented. We provide detailed performance analysis of the resulting implementation for real-valued matrices and offer recommendations for achieving high performance to future developers of dense linear algebra procedures for GPUs. Our implementation sustains 143 GFLOP/s, and we believe this is the fastest announced QR implementation executing entirely on the GPU.
Starting Page 71
Ending Page 78
Page Count 8
File Format PDF
ISBN 9781605585178
DOI 10.1145/1513895.1513904
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2009-03-08
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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