Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Emporing application programmers to make energy-aware decisions is a critical dimension of energy optimization for computer systems. ,  study the energy impact of alternative data management choices by programmers, such as data access patterns, data precision choices, and data organization. Second,  attempt to build a bridge beten application-level energy management and hardware-level energy management, by elucidating how various application-level data management features respond to Dynamic Voltage and Frequency Scal- ing (DVFS). Finally,  apply our findings to real-world applications, demonstrating their potential for guiding application-level energy opti- mization. The empirical study is particularly relevant in the Big Data era, where data-intensive applications are large energy consumers, and their energy efficiency is strongly correlated to how data are maintained and handled in programs.
['Kenan Liu', 'Gustavo Pinto', 'Yu David Liu']
I am writing a research paper on a idea Data-Oriented Characterization of Application-Level Energy Optimization
[{"from": "human", "value": "I am writing a research paper on a idea Data-Oriented Characterization of Application-Level Energy Optimization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Emporing application programmers to make energy-aware decisions is a critical dimension of energy optimization for computer systems. ,  study the energy impact of alternative data management choices by programmers, such as data access patterns, data precision choices, and data organization. Second,  attempt to build a bridge beten application-level energy management and hardware-level energy management, by elucidating how various application-level data management features respond to Dynamic Voltage and Frequency Scal- ing (DVFS). Finally,  apply our findings to real-world applications, demonstrating their potential for guiding application-level energy opti- mization. The empirical study is particularly relevant in the Big Data era, where data-intensive applications are large energy consumers, and their energy efficiency is strongly correlated to how data are maintained and handled in programs."}]