Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   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 qualitative approach to recognize the signature of executions amenable to DVFS. By recognizing the "shape of variability"  can optimize codes with highly dynamic behavior, which pose challenges to all existing DVFS techniques.  validate our approach using offline and online analyses for one-sided and two-sided communication paradigms.  have applied our methods to NWChem, and  show best case improvements in energy use of 12% at no loss in performance when using online optimizations running on 720 Hasll cores with one-sided communication. With NWChem on MPI two-sided and offline analysis, capturing the initialization,  find energy savings of up to 20%, with less than 1% performance cost.
['W. Lavrijsen', 'Costin Iancu', 'Wibe A. de Jong', 'Xin Chen', 'Karsten Schwan']
I am writing a research paper on a idea Exploiting variability for energy optimization of parallel programs
[{"from": "human", "value": "I am writing a research paper on a idea Exploiting variability for energy optimization of parallel programs"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   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 qualitative approach to recognize the signature of executions amenable to DVFS. By recognizing the \"shape of variability\"  can optimize codes with highly dynamic behavior, which pose challenges to all existing DVFS techniques.  validate our approach using offline and online analyses for one-sided and two-sided communication paradigms.  have applied our methods to NWChem, and  show best case improvements in energy use of 12% at no loss in performance when using online optimizations running on 720 Hasll cores with one-sided communication. With NWChem on MPI two-sided and offline analysis, capturing the initialization,  find energy savings of up to 20%, with less than 1% performance cost."}]