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
Published in:ACM transactions on architecture and code optimization 2021-01, Vol.18 (1), p.1-25
Main Authors: Braun, Lorenz, Nikas, Sotirios, Song, Chen, Heuveline, Vincent, Fröning, Holger
Format: Article
Language:English
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Summary: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 189 individual compute kernels from benchmarks such as Parboil, Rodinia, Polybench-GPU, and SHOC. Evaluation of the model performance using cross-validation yields a median Mean Average Percentage Error (MAPE) of 8.86–52.0% for time and 1.84–2.94% for power prediction across five different GPUs, while latency for a single prediction varies between 15 and 108 ms.
ISSN:1544-3566
1544-3973
DOI:10.1145/3431731