Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-10325
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Main Title: Accurate Energy and Performance Prediction for Frequency-Scaled GPU Kernels
Author(s): Fan, Kaijie
Cosenza, Biagio
Juurlink, Ben
Type: Article
Language Code: en
Abstract: 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.
URI: https://depositonce.tu-berlin.de/handle/11303/11444
http://dx.doi.org/10.14279/depositonce-10325
Issue Date: 27-Apr-2020
Date Available: 18-Jun-2020
DDC Class: 004 Datenverarbeitung; Informatik
Subject(s): frequency scaling
energy efficiency
GPU
modeling
Sponsor/Funder: DFG, 360291326, CELERITY: Innovative Modellierung für Skalierbare Verteilte Laufzeitsysteme
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Computation
Publisher: MDPI
Publisher Place: Basel
Volume: 8
Issue: 2
Article Number: 37
Publisher DOI: 10.3390/computation8020037
EISSN: 2079-3197
Appears in Collections:FG Architektur eingebetteter Systeme » Publications

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