基于梯度提升回归树的处理器性能数据挖掘研究  

Mining CPU Performance Data Based on Gradient Boosting Regression Tree

作　　者：吕依蓉 孙斌 喻之斌[1] Lv Yirong;SUN Bin;YU Zhibin(Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China;University of Chinese Aeademy of Seienees,Beo＇ing 100049,China;Capital Normal University Information Engineering College,Beijing 100048,China)

机构地区：[1]中国科学院深圳先进技术研究院,深圳518055 [2]中国科学院大学,北京100049 [3]首都师范大学信息工程学院,北京100048

出　　处：《集成技术》2018年第5期47-57,共11页Journal of Integration Technology

基　　金：国家重点专项项目(2016YFB1000204)

摘　　要：现代处理器一般只内置了4~8个性能计数器,但可以监测多达上千个时钟周期级别的性能事件。这些事件可以轻易地产生大量数据,称为处理器性能大数据。然而,如何从这些性能大数据中提取有价值的信息面临着许多挑战。该文提出一种处理器性能数据分析方法,通过迭代使用梯度提升回归树算法构建性能模型,为云计算负载的性能事件进行重要性排序,从而指导云计算平台的性能调优。Modern processors typically have only 4-8 performance counters which can be programmed to measure up to thousands of cycle-level performance events. These events can easily generate large amount of data, which is called central processing unit（CPU） big performance data. However, how to extract value from the big performance data faces many challenges. This paper presents a performance data analysis approach, which builds a performance model by iteratively using the gradient boosting regression tree algorithm and quantifies the importance of the performance events of workloads in cloud to guide their performance optimization.

关 键 词：性能计数器 梯度提升回归树 云计算 

分 类 号：TP302.7[自动化与计算机技术—计算机系统结构]