论文标题
一个简单的模型，用于便携式和快速预测GPU内核的执行时间和功耗
A Simple Model for Portable and Fast Prediction of Execution Time and Power Consumption of GPU Kernels
论文作者
论文摘要
在GPU上表征计算内核执行行为以进行有效的任务调度是一个非平凡的任务。我们使用一个简单的模型来解决此问题，可仅使用独立于硬件的功能在不同GPU之间进行便携式和快速预测。该模型是基于随机森林而建立的，该森林使用189个基准，例如Parboil，Rodinia，Polybench-GPU和SHOC的单独计算内核。使用交叉验证对模型性能的评估产生的平均平均百分比误差（MAPE）为8.86-52.00％和1.84-2.94％，分别在五个不同的GPU上的时间预测，而单个预测的延迟在15至108毫秒之间变化。
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.
