A Simple Model for Portable and Fast Prediction of Execution Time and Power Consumption of GPU Kernels

• URL: http://arxiv.org/abs/2001.07104v3
• Date: Wed, 30 Sep 2020 12:47:57 GMT
• Title: A Simple Model for Portable and Fast Prediction of Execution Time and Power Consumption of GPU Kernels
• Authors: Lorenz Braun, Sotirios Nikas, Chen Song, Vincent Heuveline, Holger Fr\"oning
• Abstract summary: 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 8.86-52.00% and 1.84-2.94%, for time respectively power prediction across five different GPUs, while latency for a single prediction varies between 15 and 108 milliseconds.
• Score: 2.9853894456071077
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 8.86-52.00% and 1.84-2.94%, for time respectively power prediction across five different GPUs, while latency for a single prediction varies between 15 and 108 milliseconds.

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