Accelerating Linux and Android applications on low-power devices through remote GPGPU offloading
Low-power devices are usually highly constrained in terms of CPU computing power, memory, and GPGPU resources for real-time applications to run. In this paper, we describe RAPID, a complete framework suite for computation offloading to help low-powered devices overcome these limitations. RAPID suppo...
| Autores: | , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/192756 |
| Acceso en línea: | https://hdl.handle.net/2117/192756 https://dx.doi.org/10.1002/cpe.4286 |
| Access Level: | acceso abierto |
| Palabra clave: | Distributed operating systems (Computers) Android CUDA GPGPU Mobile cloud computing Offloading Virtualization Sistemes operatius distribuïts (Ordinadors) Àrees temàtiques de la UPC::Informàtica::Sistemes operatius::Linux Àrees temàtiques de la UPC::Informàtica::Sistemes operatius::Altres sistemes operatius |
| Sumario: | Low-power devices are usually highly constrained in terms of CPU computing power, memory, and GPGPU resources for real-time applications to run. In this paper, we describe RAPID, a complete framework suite for computation offloading to help low-powered devices overcome these limitations. RAPID supports CPU and GPGPU computation offloading on Linux and Android devices. Moreover, the framework implements lightweight secure data transmission of the offloading operations. We present the architecture of the framework, showing the integration of the CPU and GPGPU offloading modules. We show by extensive experiments that the overhead introduced by the security layer is negligible. We present the first benchmark results showing that Java/Android GPGPU code offloading is possible. Finally, we show the adoption of the GPGPU offloading into BioSurveillance , a commercial real-time face recognition application. The results show that, thanks to RAPID, BioSurveillance is being successfully adapted to run on low-power devices. The proposed framework is highly modular and exposes a rich application programming interface to developers, making it highly versatile while hiding the complexity of the underlying networking layer. |
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