• DOI:10.1109/CLUSTER.2013.6702681
• Corpus ID: 6156410

A case of system-wide power management for scientific applications

@article{Liu2013ACO,
  title={A case of system-wide power management for scientific applications},
  author={Zhuo Liu and Jay F. Lofstead and Teng Wang and Weikuan Yu},
  journal={2013 IEEE International Conference on Cluster Computing (CLUSTER)},
  year={2013},
  pages={1-8}
}

• Zhuo Liu, J. Lofstead, +1 author Weikuan Yu
• Published 1 September 2013
• Computer Science
• 2013 IEEE International Conference on Cluster Computing (CLUSTER)

The advance of high-performance computing systems towards exascale will be constrained by the systems' energy consumption levels. Large numbers of processing components, memory, interconnects, and storage components must all be considered to achieve exascale performance within a targeted energy bound. While application-aware power allocation schemes for computing resources are well studied, a portable and scalable budget-constrained power management scheme for scientific applications on exascale systems is still required. Execution activities within scientific applications can be categorized as CPU-bound, I/O-bound and communication-bound. Such activities tend to be clustered into `phases', offering opportunities to manage their power consumption separately. Our experiments have demonstrated that their performance and energy consumption are affected differently by CPU frequency, an opportunity to fine tune CPU frequency for a minimal impact on the total execution time but significant savings on the energy consumption. By exploiting this opportunity, we present a phase-aware hierarchical power management framework that can opportunistically deliver good tradeoffs between system power consumption and application performance under a power budget. Our hierarchical power management framework consists of two main techniques: Phase-Aware CPU Frequency Scaling (PAFS) and opportunistic provisioning for power-constrained performance optimization. We have performed a systematic evaluation using both simulations and representative scientific applications on real systems. Our results show that our techniques can achieve 4.3%-17% better energy efficiency for large-scale scientific applications.