24831

Energy-aware Task Scheduling with Deadline Constraint in DVFS-enabled Heterogeneous Clusters

Xinxin Mei, Qiang Wang, Xiaowen Chu, Hai Liu, Yiu-Wing Leung, Zongpeng Li
Department of Computer Science, Hong Kong Baptist University
arXiv:2104.00486 [cs.DC], (1 Apr 2021)

@misc{mei2021energyaware,

   title={Energy-aware Task Scheduling with Deadline Constraint in DVFS-enabled Heterogeneous Clusters},

   author={Xinxin Mei and Qiang Wang and Xiaowen Chu and Hai Liu and Yiu-Wing Leung and Zongpeng Li},

   year={2021},

   eprint={2104.00486},

   archivePrefix={arXiv},

   primaryClass={cs.DC}

}

Energy conservation of large data centers for high-performance computing workloads, such as deep learning with big data, is of critical significance, where cutting down a few percent of electricity translates into million-dollar savings. This work studies energy conservation on emerging CPU-GPU hybrid clusters through dynamic voltage and frequency scaling (DVFS). We aim at minimizing the total energy consumption of processing a batch of offline tasks or a sequence of real-time tasks under deadline constraints. We derive a fast and accurate analytical model to compute the appropriate voltage/frequency setting for each task and assign multiple tasks to the cluster with heuristic scheduling algorithms. In particular, our model stresses the nonlinear relationship between task execution time and processor speed for GPU-accelerated applications, for more accurately capturing real-world GPU energy consumption. In performance evaluation driven by real-world power measurement traces, our scheduling algorithm shows comparable energy savings to the theoretical upper bound. With a GPU scaling interval where analytically at most 36% of energy can be saved, we record 33-35% of energy savings. Our results are applicable to energy management on modern heterogeneous clusters.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2021 hgpu.org

All rights belong to the respective authors

Contact us: