Efficient GPU Cloud architectures for outsourcing high-performance processing to the Cloud
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Título: | Efficient GPU Cloud architectures for outsourcing high-performance processing to the Cloud |
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Autor/es: | Sánchez-Ribes, Víctor | Maciá, Antonio | Mora, Higinio | Jimeno-Morenilla, Antonio |
Grupo/s de investigación o GITE: | Arquitecturas Inteligentes Aplicadas (AIA) | UniCAD: Grupo de Investigación en CAD/CAM/CAE de la Universidad de Alicante |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Tecnología Informática y Computación |
Palabras clave: | GPU | Cloud computing | High-performance processing | Offloading computation |
Fecha de publicación: | 26-may-2023 |
Editor: | Springer Nature |
Cita bibliográfica: | The International Journal of Advanced Manufacturing Technology. 2023. https://doi.org/10.1007/s00170-023-11252-0 |
Resumen: | The world is becoming increasingly dependant in computing intensive applications. The appearance of new paradigms, such as Internet of Things (IoT), and advances in technologies such as Computer Vision (CV) and Artificial Intelligence (AI) are creating a demand for high-performance applications. In this regard, Graphics Processing Units (GPUs) have the ability to provide better performance by allowing a high degree of data parallelism. These devices are also beneficial in specialized fields of manufacturing industry such as CAD/CAM. For all these applications, there is a recent tendency to offload these computations to the Cloud, using a computing offloading Cloud architecture. However, the use of GPUs in the Cloud presents some inefficiencies, where GPU virtualization is still not fully resolved, as our research on what main Cloud providers currently offer in terms of GPU Cloud instances shows. To address these problems, this paper first makes a review of current GPU technologies and programming techniques that increase concurrency, to then propose a Cloud computing outsourcing architecture to make more efficient use of these devices in the Cloud. |
Patrocinador/es: | Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was supported by the Spanish Research Agency (AEI) under project HPC4Industry PID2020-120213RB-I00. |
URI: | http://hdl.handle.net/10045/134681 |
ISSN: | 0268-3768 (Print) | 1433-3015 (Online) |
DOI: | 10.1007/s00170-023-11252-0 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1007/s00170-023-11252-0 |
Aparece en las colecciones: | INV - AIA - Artículos de Revistas INV - UNICAD - Artículos de Revistas |
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