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Article

Accurate Energy and Performance Prediction for Frequency-Scaled GPU Kernels

1
Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, Einsteinufer 17-6.0G, 10587 Berlin, Germany
2
Department of Computer Science, University of Salerno, 84084 Fisciano (Salerno), Italy
*
Author to whom correspondence should be addressed.
Computation 2020, 8(2), 37; https://doi.org/10.3390/computation8020037
Received: 6 March 2020 / Revised: 20 April 2020 / Accepted: 21 April 2020 / Published: 27 April 2020
(This article belongs to the Special Issue Energy-Efficient Computing on Parallel Architectures)
Energy optimization is an increasingly important aspect of today’s high-performance computing applications. In particular, dynamic voltage and frequency scaling (DVFS) has become a widely adopted solution to balance performance and energy consumption, and hardware vendors provide management libraries that allow the programmer to change both memory and core frequencies manually to minimize energy consumption while maximizing performance. This article focuses on modeling the energy consumption and speedup of GPU applications while using different frequency configurations. The task is not straightforward, because of the large set of possible and uniformly distributed configurations and because of the multi-objective nature of the problem, which minimizes energy consumption and maximizes performance. This article proposes a machine learning-based method to predict the best core and memory frequency configurations on GPUs for an input OpenCL kernel. The method is based on two models for speedup and normalized energy predictions over the default frequency configuration. Those are later combined into a multi-objective approach that predicts a Pareto-set of frequency configurations. Results show that our approach is very accurate at predicting extema and the Pareto set, and finds frequency configurations that dominate the default configuration in either energy or performance. View Full-Text
Keywords: frequency scaling; energy efficiency; GPU; modeling frequency scaling; energy efficiency; GPU; modeling
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MDPI and ACS Style

Fan, K.; Cosenza, B.; Juurlink, B. Accurate Energy and Performance Prediction for Frequency-Scaled GPU Kernels. Computation 2020, 8, 37. https://doi.org/10.3390/computation8020037

AMA Style

Fan K, Cosenza B, Juurlink B. Accurate Energy and Performance Prediction for Frequency-Scaled GPU Kernels. Computation. 2020; 8(2):37. https://doi.org/10.3390/computation8020037

Chicago/Turabian Style

Fan, Kaijie, Biagio Cosenza, and Ben Juurlink. 2020. "Accurate Energy and Performance Prediction for Frequency-Scaled GPU Kernels" Computation 8, no. 2: 37. https://doi.org/10.3390/computation8020037

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