Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Accelerating Linux and Android applications on low-power devices through remote GPGPU offloading. |
| Συγγραφείς: |
Montella, Raffaele, Kosta, Sokol, Oro, David, Vera, Javier, Fernández, Carles, Palmieri, Carlo, Di Luccio, Diana, Giunta, Giulio, Lapegna, Marco, Laccetti, Giuliano |
| Πηγή: |
Concurrency & Computation: Practice & Experience; 12/25/2017, Vol. 29 Issue 24, pn/a-N.PAG, 17p |
| Θεματικοί όροι: |
Linux operating systems, Cloud computing, Virtual machine systems, Central processing units, Computer storage devices, Graphics processing units, Biosurveillance |
| Reviews & Products: |
Android (Operating system) |
| Περίληψη: |
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. [ABSTRACT FROM AUTHOR] |
|
Copyright of Concurrency & Computation: Practice & Experience is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Βάση Δεδομένων: |
Complementary Index |