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A Simple Model for Portable and Fast Prediction of Execution Time and Power Consumption of GPU Kernels
arXiv - CS - Performance Pub Date : 2020-01-20 , DOI: arxiv-2001.07104
Lorenz Braun, Sotirios Nikas, Chen Song, Vincent Heuveline, Holger Fr\"oning

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.

中文翻译:

GPU 内核执行时间和功耗的便携式和快速预测的简单模型

表征 GPU 上的计算内核执行行为以实现高效的任务调度是一项非常重要的任务。我们通过一个简单的模型来解决这个问题,该模型仅使用独立于硬件的功能即可在不同的 GPU 之间进行可移植和快速的预测。该模型基于随机森林构建,使用来自 Parboil、Rodinia、Polybench-GPU 和 SHOC 等基准测试的 189 个独立计算内核。使用交叉验证评估模型性能产生 8.86-52.00% 和 1.84-2.94% 的中位数平均百分比误差 (MAPE),分别用于五个不同 GPU 的时间和功率预测,而单个预测的延迟在 15 到 15 之间变化108 毫秒。
更新日期:2020-10-01
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