静态程序切片的GPU通用计算功耗预测模型

摘要

随着图形处理器通用计算的发展,GPU(graphics processing unit)通用计算程序功耗的度量与优化成为绿色计算领域中的一个基础问题。当前,GPU 计算能耗评测主要通过硬件来实现,而开发人员无法在编译之前了解应用程序能耗,难以实现能耗约束下的代码优化与重构。为了解决开发人员评估应用程序能耗的问题,提出了针对应用程序源代码的静态功耗预测模型,根据分支结构的疏密程度以及静态程序切片技术,分别建立分支稀疏和稠密两类应用程序的功耗预测模型。程序切片是介于指令与函数之间的度量粒度,在分析GPU应用程序时具有较强的理论支持和可行性。用非线性回归和小波神经网络建立两种切片功耗模型。针对特定 GPU 非线性回归模型的准确性较好。小波神经网络预测模型适合各种体系的 GPU,具有较好的通用性。对应用程序分支结构进行分析后,为分支稀疏程序提供加权功率统计模型,以保证功耗评估算法的效率。分支稠密程序则采用基于执行路径概率的功耗预测法,以提高预测模型的准确性。实验结果表明,两种预测模型及算法能够有效评估 GPU 通用计算程序的功耗,模型预测值与实际测量值的相对误差低于6%。%With the development of general-purpose computing of GPUs (graphics processing units), power consumption measurements and optimization have become an essential issue in the green computing field. The current power consumption of GPUs is mainly measured by the hardware. However the programmers have had difficulty understanding the power consumption profile of the applications used to optimize and refactor before the compile phase. To solve this issue, power consumption models were proposed for GPU applications with regard to sparseness-branch and denseness-branch programs based on program slicing, respectively. The program slicing is fine-grained level that lies between the function and the instruction levels and has good feasibility and accuracy in the power consumption estimation. The power consumption prediction models for program slicing were proposed through no-linear regression and wavelet neural networks. To specific GPUs, the power prediction model based on no-linear regression is more precise than the prediction model based on wavelet neural networks. However the wavelet neural networks model has better generality to various kinds of GPUs. After analyzing the structure of the applications, the weighted power model for sparseness-branch programs was provided to achieve better effectiveness. The probability slicing power model for denseness-branch programs was also proposed to improve the accuracy that is based on the probability of the execution paths. The results indicate that the two different models can effectively predict the power consumption. And the average relative error between the predicted value and the measured value is less than 6%.