Energy-Aware Non-Preemptive Task Scheduling With Deadline Constraint in DVFS-Enabled Heterogeneous Clusters

Qiang Wang, Xinxin Mei, Hai Liu, Yiu Wing Leung, Zongpeng Li, Xiaowen Chu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)4083-4099
Number of pages17
JournalIEEE Transactions on Parallel and Distributed Systems
Volume33
Issue number12
Early online date8 Jun 2022
DOIs
Publication statusE-pub ahead of print - 8 Jun 2022

Scopus Subject Areas

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics

User-Defined Keywords

  • Dynamic Voltage and Frequency Scaling
  • Graphics Processing Units
  • Task Scheduling

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