Improving CADNA Performance on GPUs

Abstract : The quantification of rounding errors is crucial for numerical simulations on massively parallel architectures such as GPUs. The CADNA library enables one to estimate rounding errors in simulation programs. A version of CADNA for GPUs had been proposed to show the feasibility of numerical validation on such architectures. In this paper we show how the performance of CADNA on GPUs has been improved. Thanks to various optimizations that have been validated on several benchmarks, the performance gain is up to 61% with respect to the original prototype. Furthermore the GPU version of CADNA has been completed with features such as the accuracy estimation for double precision computation.
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https://hal.sorbonne-universite.fr/hal-01858537
Contributor : Pierre Fortin <>
Submitted on : Monday, August 20, 2018 - 10:57:33 PM
Last modification on : Friday, July 5, 2019 - 3:26:03 PM

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Pacôme Eberhart, Baptiste Landreau, Julien Brajard, Pierre Fortin, Fabienne Jézéquel. Improving CADNA Performance on GPUs. 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), May 2018, Vancouver, Canada. pp.1016-1025, ⟨10.1109/IPDPSW.2018.00156⟩. ⟨hal-01858537⟩

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