GPUWattch: enabling energy optimizations in GPGPUs

By Jingwen Leng, Tayler H. Hetherington, Ahmed Eltantawy, S. Gilani, Nam Sung Kim, Tor M. Aamodt, Vijay Janappa Reddi. International Symposium on Computer Architecture, 2013

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Convolution engine: balancing efficiency & flexibility in specialized computing (2013, methodology): “264 SAD algorithm [28] using GPUGPUSim simulator [6] with GPUWattch energy model [21].”
Data Center Energy Consumption Modeling: A Survey (2016, methodology): “GPUWattch methodology to build power models [175].”
ACT: designing sustainable computer systems with an architectural carbon modeling tool (2022, background): “Unfortunately, unlike the myriad available tools to quantify hardware energy consumption [13, 20, 25, 47, 51, 53], there is a distinct lack of methodologies to understand the carbon footprint of system hardware.”
Understanding the Future of Energy Efficiency in Multi-Module GPUs (2019, methodology): “Commonly used GPU energy estimation frameworks adopt a bottom-up modeling approach [15], [24], where the energy cost of each key microarchitectural component is combined with the corresponding switching activities to determine the overall GPU energy consumption.”