Exploiting variability for energy optimization of parallel programs

Wim Lavrijsen, Costin Iancu, Wibe de Jong, Xin Chen, Karsten Schwan
2016 Proceedings of the Eleventh European Conference on Computer Systems - EuroSys '16  
In this paper we present optimizations that use DVFS mechanisms to reduce the total energy usage in scientific applications. Our main insight is that noise is intrinsic to large scale parallel executions and it appears whenever shared resources are contended. The presence of noise allows us to identify and manipulate any program regions amenable to DVFS. When compared to previous energy optimizations that make per core decisions using predictions of the running time, our scheme uses a
more » ... e approach to recognize the signature of executions amenable to DVFS. By recognizing the "shape of variability" we can optimize codes with highly dynamic behavior, which pose challenges to all existing DVFS techniques. We validate our approach using offline and online analyses for one-sided and two-sided communication paradigms. We have applied our methods to NWChem, and we show best case improvements in energy use of 12% at no loss in performance when using online optimizations running on 720 Haswell cores with one-sided communication. With NWChem on MPI two-sided and offline analysis, capturing the initialization, we find energy savings of up to 20%, with less than 1% performance cost. Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royaltyfree right to publish or reproduce this article, or to allow others to do so, for Government purposes only.
doi:10.1145/2901318.2901329 dblp:conf/eurosys/LavrijsenIJCS16 fatcat:kh3p5fzmqff3pkqelrjisuw724