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Modern computing paradigms, such as cloud computing, are increasinglyadopting GPUs to boost their computing capabilities primarily due to theheterogeneous nature of AI/ML/deep learning workloads. However, the energyconsumption of GPUs is a critical problem. Dynamic Voltage Frequency Scaling(DVFS) is a widely used technique to reduce the dynamic power of GPUs. Yet,configuring the optimal clock frequency for essential performance requirementsis a non-trivial task due to the complex nonlinear relationship between theapplication's runtime performance characteristics, energy, and execution time.It becomes more challenging when different applications behave distinctivelywith similar clock settings. Simple analytical solutions and standard GPUfrequency scaling heuristics fail to capture these intricacies and scale thefrequencies appropriately. In this regard, we propose a data-driven frequencyscaling technique by predicting the power and execution time of a givenapplication over different clock settings. We collect the data from applicationprofiling and train the models to predict the outcome accurately. The proposedsolution is generic and can be easily extended to different kinds of workloadsand GPU architectures. Furthermore, using this frequency scaling by predictionmodels, we present a deadline-aware application scheduling algorithm to reduceenergy consumption while simultaneously meeting their deadlines. We conductreal extensive experiments on NVIDIA GPUs using several benchmark applications.The experiment results have shown that our prediction models have high accuracywith the average RMSE values of 0.38 and 0.05 for energy and time prediction,respectively. Also, the scheduling algorithm consumes 15.07% less energy ascompared to the baseline policies.

A Data-Driven Frequency Scaling Approach for Deadline-aware Energy Efficient Scheduling on Graphics Processing Units (GPUs)