Name
Title | ||
|---|---|---|
Heterogeneous Secure Multi-level Remote Acceleration Service for Low-power Integrated Systems and Devices. |
Abstract | ||
|---|---|---|
This position paper presents a novel heterogeneous CPU-GPU multi-level cloud acceleration focusing on applications running on embedded systems found on low-power devices. A runtime system performs energy and performance estimations in order to automatically select local CPU-based and GPU-based tasks that should be seamlessly executed on more powerful remote devices or cloud infrastructures. Moreover, it proposes, for the first time, a secure unified model where almost any device or infrastructure can operate as an accelerated entity and/or as an accelerator serving other less powerful devices in a secure way. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.procs.2016.08.287 | Procedia Computer Science |
Keywords | Field | DocType |
HPC,accelerators,devices,offloading,QoS,Cloud computing | Computer science,Position paper,Quality of service,Acceleration,Integrated systems,Unified Model,Embedded system,Runtime system,Cloud computing | Conference |
Volume | ISSN | Citations |
97 | 1877-0509 | 2 |
PageRank | References | Authors |
0.36 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lara Lopez | 1 | 2 | 0.70 |
| Francisco Javier Nieto | 2 | 34 | 3.88 |
| Terpsichori Helen Velivassaki | 3 | 7 | 1.18 |
| Sokol Kosta | 4 | 276 | 24.92 |
| Cheol-Ho Hong | 5 | 115 | 10.66 |
| Raffaele Montella | 6 | 210 | 23.13 |
| Iakovos Mavroidis | 7 | 18 | 1.17 |
| Carles Fernández | 8 | 22 | 3.01 |