Energy-Aware Scheduling of Parallel Application in Hybrid Computing System

MA Yan; GONG Bin; GUO Zhihong; CHEN Yufeng; ZOU Lida

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Article Navigation > Chinese Journal of Electronics > 2014 > 23(4): 688-694

MA Yan, GONG Bin, GUO Zhihong, et al., “Energy-Aware Scheduling of Parallel Application in Hybrid Computing System,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 688-694, 2014,

Citation:

MA Yan, GONG Bin, GUO Zhihong, et al., “Energy-Aware Scheduling of Parallel Application in Hybrid Computing System,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 688-694, 2014,

MA Yan, GONG Bin, GUO Zhihong, et al., “Energy-Aware Scheduling of Parallel Application in Hybrid Computing System,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 688-694, 2014,

Citation:

MA Yan, GONG Bin, GUO Zhihong, et al., “Energy-Aware Scheduling of Parallel Application in Hybrid Computing System,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 688-694, 2014,

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Energy-Aware Scheduling of Parallel Application in Hybrid Computing System

1.
State Grid Shandong Electric Power Research Institute, Jinan 250002, China;
2.
School of Computer Science and Technology, Shandong University, Jinan 250101, China

Received Date: 2014-01-01
Rev Recd Date: 2014-02-01
Publish Date: 2014-10-05

Abstract

Abstract

Energy management is emerging as an important issue for High performance computing (HPC) owning to high operational cost and low reliability. Compared with low-power architectural approach, energy-aware scheduling based on Dynamic voltage scaling (DVS) and Dynamic power management (DPM) is regarded as a promising way since it is practical and low-cost. At present, most studies focus on pure DVS or non-DVS environment, while most high performance computing systems are hybrid non-DVS/DVS platforms. We propose an energy-aware scheduling algorithm for parallel application to consider both DVS and non-DVS characteristics of hybrid system. We present the rule of task assignment, make analysis on DVS and DPM technique and give their mathematical formulation, which maintains makespan optimization and energy conservation. The clustering and merging algorithm, and priority computation method consider the situation of resource constraints. The extensive simulations demonstrate that the proposed algorithm has stronger ability of energy saving and time optimization than Heterogeneous earliest finish time (HEFT), Energy-efficient task duplication scheduling (EETDS) and Heterogeneous energy-aware duplication scheduling (HEADUS) algorithm no matter for synthetic workload or realistic workload.
- Energy efficiency,
- Dependent tasks,
- Multiprocessor systems

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References(13)

References

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