Abstract
Accelerating applications with Graphics Processing Units (GPUs) is stimulating ongoing interest in diverse fields within science and engineering. The Compute Unified Data Architecture (CUDA) paradigm introduced by Nvidia in 2007 is arguably the most significant driver in the uptake of GPUs for general purpose computation. However, a large array of entry points to GPU acceleration also exists in the form of libraries, explicit applications, as well as compiler directives. An important computational task for many applications is matrix decomposition; this chapter presents new CUDA implementations of key algorithms, specifically LU and QR matrix decomposition for batches of small, dense matrices.
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Acknowledgements
The authors would like to thank Muhammed Kabiru Hassan and Sreejith Ganesh Jaya for bringing applications to their attention that benefit from the routines detailed here. The authors are also very grateful to reviewers from Nvidia for their comments and improvements to the manuscript.
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Brouwer, W.J., Taunay, PY. (2014). Efficient Batch LU and QR Decomposition on GPU. In: Kindratenko, V. (eds) Numerical Computations with GPUs. Springer, Cham. https://doi.org/10.1007/978-3-319-06548-9_4
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DOI: https://doi.org/10.1007/978-3-319-06548-9_4
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