Document Type
Conference Proceeding
Language
eng
Format of Original
10 p.
Publication Date
12-6-2013
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Source Publication
2013 IEEE 32nd International Performance Computing and Communications Conference (IPCCC)
Source ISSN
9781479932139
Original Item ID
doi: 10.1109/PCCC.2013.6742766
Abstract
Featured by high portability and programmability, Dynamic Voltage and Frequency Scaling (DVFS) has been widely employed to achieve energy efficiency for high performance applications on distributed-memory architectures nowadays through various scheduling algorithms. Generally, different forms of slack from load imbalance, network latency, communication delay, memory and disk access stalls, etc. are exploited as energy saving opportunities where peak CPU performance is not necessary, with little or limited performance loss. The deployment of DVFS for communication intensive applications is straightforward due to the explicit boundary between Energy Saving Blocks (ESBs) at source code level, while for data (e.g., memory and disk access) intensive applications it is difficult for applying DVFS since ESB boundary is implicit due to mixed types of workloads. We propose an adaptively aggressive DVFS scheduling strategy to achieve energy efficiency for data intensive applications, and further save energy via speculation to mitigate DVFS overhead for imbalanced branches. We implemented and evaluated our approach using five memory and disk access intensive benchmarks with imbalanced branches against another two energy saving approaches. The experimental results indicate an average of 32.6% energy savings were achieved with 6.2% average performance loss compared to the original executions on a power-aware 64-core cluster.
Recommended Citation
Tan, Li; Chen, Zizhong; Zong, Ziliang; Ge, Rong; and Li, Dong, "A2E: Adaptively Aggressive Energy Efficient DVFS Scheduling for Data Intensive Applications" (2013). Mathematics, Statistics and Computer Science Faculty Research and Publications. 199.
https://epublications.marquette.edu/mscs_fac/199
Comments
Accepted version. Published as part of the proceedings of the conference, 2013 IEEE 32nd International Performance Computing and Communications Conference (IPCCC), 2013. DOI. © 2013 IEEE. Used with permission.