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Erschienen in:
An Experience Report on (Auto-)tuning of Mesh-Based PDE Solvers on Shared Memory Systems Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Using GPGPU Accelerated Interpolation Algorithms for Marine Bathymetry Processing with On-Premises and Cloud Based Computational Resources

verfasst von : Livia Marcellino, Raffaele Montella, Sokol Kosta, Ardelio Galletti, Diana Di Luccio, Vincenzo Santopietro, Mario Ruggieri, Marco Lapegna, Luisa D’Amore, Giuliano Laccetti

Erschienen in: Parallel Processing and Applied Mathematics

Verlag: Springer International Publishing

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Abstract

Data crowdsourcing is one of most remarkable results of pervasive and internet connected low-power devices making diverse and different “things” as a world wide distributed system. This paper is focused on a vertical application of GPGPU virtualization software exploitation targeted on high performance geographical data interpolation. We present an innovative implementation of the Inverse Distance Weight (IDW) interpolation algorithm leveraging on CUDA GPGPUs. We perform tests in both physical and virtualized environments in order to demonstrate the potential scalability in production. We present an use case related to high resolution bathymetry interpolation in a crowdsource data context.

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Vorheriges Kapitel An Experience Report on (Auto-)tuning of Mesh-Based PDE Solvers on Shared Memory Systems
Nächstes Kapitel Relaxing the Correctness Conditions on Concurrent Data Structures for Multicore CPUs. A Numerical Case Study
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Metadaten
Titel
Using GPGPU Accelerated Interpolation Algorithms for Marine Bathymetry Processing with On-Premises and Cloud Based Computational Resources
verfasst von
Livia Marcellino
Raffaele Montella
Sokol Kosta
Ardelio Galletti
Diana Di Luccio
Vincenzo Santopietro
Mario Ruggieri
Marco Lapegna
Luisa D’Amore
Giuliano Laccetti
Copyright-Jahr
2018
Buch
Parallel Processing and Applied Mathematics

Print ISBN: 978-3-319-78053-5

Electronic ISBN: 978-3-319-78054-2

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-78054-2_2