摘要
论文提出一种基于人工神经网络的线程数据加速划分模型。利用该模型学习样本集中隐含的划分知识,来指导新程序的划分。样本中的知识用样本的特征和划分方案组成,它们分别作为网络的输入和输出来训练网络,直至达到指定的精度。在测试阶段,利用测试程序预执行获取的特征信息作为模型输入,运行模型得出该程序的划分方案,作为预测结果。基于Prophet实验平台,该文提出的模型有效地预测出待划分程序的划分方案,平均预测精度约为0.7。而且依据该划分方案指导程序划分后,加速比性能比原有划分方案获得的加速比最大提升了11.8%。实验证明了论文提出的模型在对未知程序划分方案预测方面是有效的。
This paper presents an artificial neural network based on thread data acceleration partition model.This model is used to learn the partitioning knowledge implicit in the sample set to guide the division of new procedures.The knowledge in the sample is composed of sample characteristics and division schemes.They train the network as the input and output of the network,respectively,until the specified accuracy is reached.In the test phase,the test program is used to pre-execute the acquired characteristic information as a model input.The operation model is used to obtain a division scheme of the program as a prediction result.Based on the Prophet experimental platform,the model proposed in this paper effectively predicts the division scheme of the program to be divided,and the average prediction accuracy is about 0.7.Moreover,according to the division scheme guiding program,the acceleration ratio obtained by the acceleration ratio ratio is higher by 11.8% than the original division scheme.The experiment proves that the model presented in this paper is effective in predicting the unknown program partitioning scheme.
作者
盛红雷
贾崟
SHENG Honglei;JIA Yin(Nari Group Corporation Information & Communication Technology Company,Nanjing 210000;State Grid Jibei Electric Power Company Limited,Beijing 100054)
出处
《舰船电子工程》
2019年第1期85-89,共5页
Ship Electronic Engineering
关键词
系统结构
人工神经网络
样本集
划分方案
预测模型
system structure
artificial neural network
sample set
partition scheme
prediction model
