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 at a subsystem-level 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-A recent study shows that computation per kilowatthour has doubled every 1.57 years, akin to Moore's Law. While this trend is encouraging, its implications to high-performance computing (HPC) are not yet clear. For instance, DARPA's target of a 20-MW exaflop system will require a 56.8-fold performance improvement with only a 2.4-fold increase in power consumption, which seems unachievable in light of the above trend. To provide a more comprehensive perspective, we analyze current trends in energy efficiency from the Green500 and project expectations for the near future.Specifically, we first provide an analysis of energy efficiency trends in HPC systems from the Green500. We then model and forecast the energy efficiency of future HPC systems. Next, we present exascalar -a holistic metric to measure the distance from the exaflop goal. Finally, we discuss our efforts to standardize power measurement methodologies in order to provide the community with reliable and accurate efficiency data.
In recent years, the high-performance computing (HPC) community has recognized the need to design energy-efficient HPC systems. The main focus, however, has been on improving the energy efficiency of computation, resulting in an oversight on the energy efficiencies of other aspects of the system such as memory or disks. Furthermore, the energy consumption of the non-computational parts of a HPC system continues to consume an increasing percentage of the overall energy consumption. Therefore, to capture a more accurate picture of the energy efficiency of a HPC system, we seek to create a benchmark suite and associated methodology to stress different components of a HPC system, such as the processor, memory, and disk. Doing so, however, results in a potpourri of benchmark numbers that make it difficult to "rank" the energy efficiency of HPC systems. This leads to the following question: What metric, if any, can capture the energy efficiency of a HPC system with a single number?To address the above, we propose The Green Index (TGI), a metric to capture the system-wide energy efficiency of a HPC system as a single number. Then, in turn, we present (1) a methodology to compute TGI, (2) an evaluation of system-wide energy efficiency using TGI, and (3) a preliminary comparison of TGI to the traditional performance-to-power metric, i.e., floating-point operations per second (FLOPS) per watt.
Abstract. The computing community is facing several big data challenges due to the unprecedented growth in the volume and variety of data. Many large-scale Internet companies use distributed NoSQL data stores to mitigate these challenges. These NoSQL data-store installations require massive computing infrastructure, which consume significant amount of energy and contribute to operational costs. This cost is further aggravated by the lack of energy proportionality in servers. Therefore, in this paper, we study the energy proportionality of servers in the context of a distributed NoSQL data store, namely Apache Cassandra. Towards this goal, we measure the power consumption and performance of a Cassandra cluster. We then use power and resource provisioning techniques to improve the energy proportionality of the cluster and study the feasibility of achieving an energy-proportional data store. Our results show that a hybrid (i.e., power and resource) provisioning technique provides the best power savings -as much as 55%.
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