Name
Abstract | ||
|---|---|---|
In this paper, we undertake the problem of minimizing system-level energy on chip-multicore processors (CMPs) executing a periodic real-time workload. Our framework has two components: i.) a static phase that selects a subset of cores upon which the workload can be executed without dissipating excessive static power and performs task-to-core allocation, ii.) a dynamic phase that involves managing the selected cores at run-time through coordinated power management framework that exploits Dynamic Voltage and Frequency Scaling (DVFS) as well as multiple idle states offered by modern CMP architectures, to reduce the dynamic power. We explicitly consider the unique traits of the currently available CMP architectures that distinguish them from multiprocessors, including the unique voltage level shared by the cores and its implications for DVFS. We identify the global energy-efficient frequency which indicates the minimum frequency level at which concurrent execution on multiple cores should take place to preserve the efficiency of DVFS. Then we propose two algorithms CVFS and CVFS* to minimize the dynamic energy consumption through concerted use of DVFS and idle states. Our experimental evaluation indicates that our framework can provide significant gains in system energy. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/GREENCOMP.2010.5598261 | Green Computing Conference |
Keywords | Field | DocType |
excessive static power,global energy-efficient frequency,available cmp architecture,algorithms cvfs,dynamic phase,system energy,chip multiprocessors,power management framework,periodic real-time task,coordinated power management,dynamic power,dynamic energy consumption,system-level energy,real time systems,frequency scaling,energy efficient,time frequency analysis,chip,multicore processing,real time,multicore processors,energy management,resource management,sleep | Dynamic voltage scaling,Energy management,Power management,Workload,Computer science,Parallel computing,Dynamic demand,Frequency scaling,Multi-core processor,Energy consumption,Distributed computing | Conference |
ISBN | Citations | PageRank |
978-1-4244-7612-1 | 18 | 0.78 |
References | Authors | |
23 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vinay Devadas | 1 | 179 | 6.94 |
| Hakan Aydin | 2 | 1218 | 61.97 |