Fine-Grained Energy Consumption Characterization and Modeling
Fine-Grained Energy Consumption Characterization and Modeling
复制标题
细粒度的能耗表征和建模
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
M. Laurenzano
中科院分区：
文献类型：
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作者：
C. Olschanowsky;T. Rosing;A. Snavely;L. Carrington;M. Tikir;M. Laurenzano
能源成本占大型超级计算机总拥有成本的很大一部分。与性能一样，能源效率并不是计算资源的单独属性;它是资源-工作负载组合的函数。工作负载中应用的操作组合和局部性特征影响资源的能量消耗。我们的实验证实，数据局部性是能源需求变化的主要来源。这项工作的主要贡献包括高性能计算（HPC）资源上执行细粒度的功率测量的方法，一个基准的基础设施，行使特定部分的节点，以表征操作的能源成本，和一种方法相结合的应用程序信息与独立的能源测量，以估计特定的应用程序资源配对的能源需求。使用NAS并行基准测试和S3 D的验证研究表明，我们的模型具有7.4%的平均预测误差。
Energy costs comprise a significant fraction of the total cost of ownership of a large supercomputer. As with performance, energy-efficiency is not an attribute of a compute resource alone; it is a function of a resource-workload combination. The operation mix and locality characteristics of the applications in the workload affect the energy consumption of the resource. Our experiments confirm that data locality is the primary source of variation in energy requirements. The major contributions of this work include a method for performing fine-grained power measurements on high performance computing (HPC) resources, a benchmark infrastructure that exercises specific portions of the node in order to characterize operation energy costs, and a method of combining application information with independent energy measurements in order to estimate the energy requirements for specific application-resource pairings. A verification study using the NAS parallel benchmarks and S3D shows that our model has an average prediction error of 7.4%.