SPECpower

Abstract: Massive data centers housing thousands of computing nodes have become commonplace in enterprise computing, and the power consumption of such data centers is growing at an unprecedented rate. Adding to the problem is the inability of the servers to exhibit energy proportionality, i.e., provide energy-efficient execution under all levels of utilization, which diminishes the overall energy efficiency of the data center. It is imperative that we realize effective strategies to control the power consumption of the server and improve the energy efficiency of data centers. With the advent of Intel Sandy Bridge processors, we have the ability to specify a limit on power consumption during runtime, which creates opportunities to design new power-management techniques for enterprise workloads and make the systems that they run on more energy proportional.In this paper, we investigate whether it is possible to achieve energy proportionality for an enterprise-class server workload, namely SPECpower_ssj2008 benchmark, by using Intel's Running Average Power Limit (RAPL) interfaces. First, we analyze the power consumption and characterize the instantaneous power profile of the SPECpower benchmark within different subsystems using the on-chip energy meters exposed via the RAPL interfaces. We then analyze the impact of RAPL power limiting on the performance, per-transaction response time, power consumption, and energy efficiency of the benchmark under different load levels. Our observations and results shed light on the efficacy of the RAPL interfaces and provide guidance for designing power-management techniques for enterprise-class workloads.

Abstract: According to the United States Environmental Protection Agency (US EPA) almost 3% of all electricity consumed within the US in 2010 goes to running datacenters, with the majority of that powering servers and the associated air conditioning systems dedicated to eliminating the heat they produce. The EPA launched the ENERGY STAR® Computer Server program in May 2009, intended to deliver information to better enable server purchasing decisions based on projected power consumption.The Server Efficiency Rating Tool (SERT) has been developed by the Standard Performance Evaluation Corporation (SPEC) SPECpower committee to address the EPA requirements for Version 2 of the ENERGY STAR server program. Unlike many tools sourced from the SPEC organization the SERT is not intended to be a benchmark, and for Version 2 does not offer a single score model. Instead it produces detailed information regarding the influence of CPU, memory, network and storage I/O configurations on overall server power consumption.This paper describes the design and development of the SERT, including discussion of the collaborative nature of working with the EPA and the various industry stakeholders involved in the design, review and development process. Many of the core ideas behind SERT were derived from theSPECpower_ssj2008 and other SPEC-developed benchmarks, and this paper illustrates where ideas and code were shared, as well as where new thinking resulted in entirely new solutions. It also includes thoughts for the future, as the ENERGY STAR server program continues to evolve and the SERT will evolve with it.

Abstract: SPEC has recently released SPECpower_ssj2008, the first industry benchmark which measures performance and power of volume server class computers using graduated load levels. In this paper, we present a brief overview and an initial characterization of this benchmark by measuring the system resource utilization with the aid of processor monitoring events at graduated load levels and by comparing the sensitivity of final metric and other related data between various configurations consisting of hardware changes as well as software changes on Quad Core Intel Xeon processor based servers. Even though this is early data from a specific platform and OS, it still validates many expected patterns and opens exciting new opportunities for researchers to investigate specific areas as well as in-depth characterization as a next step.