众核处理器片内资源利用率和能效优化研究

项目名称： 众核处理器片内资源利用率和能效优化研究

项目编号： No.61202059

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 计算机科学学科

项目作者： 宋风龙

作者单位： 中国科学院计算技术研究所

项目金额： 25万元

中文摘要： 功耗墙是众核处理器结构扩展和性能发挥的极大挑战。新型网络应用是现代计算系统的最重要应用之一，其三个动态时变特征，即高通量需求、大地址空间的离散访问、交互式请求驱动的执行模式，导致众核芯片内共享资源竞争严重，资源有效利用率的降低使能耗问题更为突出。针对此问题，本课题将通过捕捉特征的动态时变规律，研究感知能耗需求的片内资源管理方法，在功耗约束下实现片内资源的高效利用。主要研究内容包括：（1）需求特征提取：通过迭代优化，研究特征降维的聚类方法，分析新型网络应用对片内资源和能耗的时变需求特征；（2）时变需求精确预测：利用程序的需求特征，通过博弈论方法，研究精确预测片内资源和能耗动态时变需求的方法，捕捉需求特征的动态时变规律；（3）软硬件协同管理：利用能耗和片内资源的时变需求预测，通过片内资源动态分配和线程调度技术，研究片内资源有效利用率的动态优化方法，减少资源竞争访问，实现功耗约束下的性能提升。

中文关键词： 众核处理器；功耗墙；资源利用率；运行时特征；能耗感知的资源管理

英文摘要： "Power Wall" is critical to scalability and performance of many-core architecture, and it has been a fatal challenge to processor design. The newly high throughput Internet applications, such as social network, are one of the most important fields for modern computing systems, which is correlative to our daily life closely. The typical characteristics of such high throughput Internet applications are interactive request driven, and discrete data fields accessing in a large memory address space concurrently. The execution mode results in severe collision of shared module frequently, and lower effective utility rate of on-chip hardware resources and then wasting energy. It has become a critical limit to many-core processor's performance improvement and architecture scalable. Aimed at the above problem, we are going to propose an energy-aware on-chip hardware resource management policy via catching time-varying request of on-chip hardware resource and energy dynamically, which will use on-chip resources efficiently under the constraints of energy consumption. There are three points in our research. Firstly, for difficult to distill characteristics of application's demands of on-chip hardware resources and energy, we are going to research how to reduce feature dimensions of clustering method via iterative optimizati

英文关键词： Many-core Architecture；Power Wall；On-chip Resource Utilization；Runtime Characteristics；Energy-aware Resource Management