Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Data-Driven Frequency Scaling Approach for Deadline-aware Energy
  Efficient Scheduling on Graphics Processing Units (GPUs)   Modern computing paradigms, such as cloud computing, are increasingly
adopting GPUs to boost their computing capabilities primarily due to the
heterogeneous nature of AI/ML/deep learning workloads. However, the energy
consumption 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 requirements
is a non-trivial task due to the complex nonlinear relationship between the
application's runtime performance characteristics, energy, and execution time.
It becomes more challenging when different applications behave distinctively
with similar clock settings. Simple analytical solutions and standard GPU
frequency scaling heuristics fail to capture these intricacies and scale the
frequencies appropriately. In this regard, we propose a data-driven frequency
scaling technique by predicting the power and execution time of a given
application over different clock settings. We collect the data from application
profiling and train the models to predict the outcome accurately. The proposed
solution is generic and can be easily extended to different kinds of workloads
and GPU architectures. Furthermore, using this frequency scaling by prediction
models, we present a deadline-aware application scheduling algorithm to reduce
energy consumption while simultaneously meeting their deadlines. We conduct
real extensive experiments on NVIDIA GPUs using several benchmark applications.
The experiment results have shown that our prediction models have high accuracy
with 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 as
compared to the baseline policies.

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The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ex
3
The categories of this paper are: physics
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-lat
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The categories of this paper are: plasm-ph
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The categories of this paper are: econ
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The categories of this paper are: q-fin
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The categories of this paper are: supr-con
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The categories of this paper are: nucl-ex