| 摘要: |
| 当前,很多部门使用高性能计算机周期性地进行业务性的数值计算.维护这些业务系统的主要代价是每天消耗的大量电能,降低能量消耗能够极大地降低维护业务系统的成本.高性能业务系统的核心是微处理器,当前,微处理器普遍支持动态电压调节技术.该技术通过降低微处理器的电压和频率减小微处理器的能耗,但是一般会导致系统性能的下降.提出了一种面向高性能业务应用的能量优化技术.该技术利用系统支持的多个频率层次,建立性能约束下的能量优化模型,优化业务应用的能耗.根据程序信息获取方式的差别,提出了SEOM 和CEOM 两种能量优化模型,SEOM模型的程序信息可以直接测试获取,CEOM的程序信息采用编译器插桩方法获取.使用典型平台对能耗优化效果进行了验证,最多可节省12%的能耗. |
| 关键词: 业务系统 动态电压频率调节 剖视信息 能量模型 |
| DOI：10.3724/SP.J.1001.2013.04363 |
| 分类号: |
| 基金项目:国家自然科学基金(60903059, 61003087, 61170049); 国家高技术研究发展计划(863)(2012AA01A301, 2012AA010903); 国家科技重大专项(2009ZX01036-001-003-001) |
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| Profile-Guided Optimization of System Energy Consumption for High-Performance Operational Applications |
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YI Hui-Zhan, LUO Zhao-Cheng
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College of Computer, National University of Defense Technology, Changsha 410073, China
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| Abstract: |
| Currently many high-performance computers are used to finish operational numerical computing cyclically. The main maintenance cost originates from the large amount of electric energy, and reducing energy consumption can reduce the maintenance cost significantly. The core units for operational systems are microprocessors, and the current microprocessors prevalently support the low power technique of the dynamic voltage and frequency scaling (DVFS). DVFS reduces the energy consumption by decreasing the supply voltage and execution frequency, which generally leads to performance reduction. This paper models energy consumption of operational applications confined by time constraints, and present energy optimization techniques by DVFS in the operational systems. Differing on the way to obtain the program execution information, two energy optimization models, SEOM and CEOM are evolved. The execution information of SEOM is obtained directly from testing, and the execution information of CEOM is obtained from compiler-directed program profile. The models have been investigated in representative computer platforms, and the results show that they can save 12% the largest reduction of energy consumption. |
| Key words: operational system dynamic voltage and frequency scaling profile information energy model |
