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A simple model for portable and fast prediction of execution time and power consumption of GPU Kernels

Characterizing compute kernel execution behavior on GPUs for efficient task scheduling is a non-trivial task. We address this with a simple model enabling portable and fast predictions among different GPUs using only hardware-independent features. This model is built based on random forests using 18...

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Bibliographic Details
Main Authors: Braun, Lorenz (Author) , Nikas, Sotirios (Author) , Song, Chen (Author) , Heuveline, Vincent (Author) , Fröning, Holger (Author)
Format: Article (Journal)
Language:English
Published: 2021
In: ACM Transactions on architecture and code optimization
Year: 2021, Volume: 18, Issue: 1, Pages: 1-25
ISSN:1544-3973
DOI:10.1145/3431731
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1145/3431731
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Author Notes:Lorenz Braun (Institute of Computer Engineering, Heidelberg University, Germany), Sotirios Nikas, Chen Song, and Vincent Heuveline (Engineering Mathematics and Computing Lab, Heidelberg University, Germany), Holger Fröning (Institute of Computer Engineering, Heidelberg University, Germany)

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