For current High Performance Computing systems to scale towards the holy grail of ExaFLOP performance, their power consumption has to be reduced by at least one order of magnitude. This goal can be achieved only through a combination of hardware and software advances. Being able to model and accurately predict the power consumption of large computational systems is necessary for software-level innovations such as proactive and power-aware scheduling, resource allocation and fault tolerance techniques. In this paper we present a 2-layer model of power consumption for a hybrid supercomputer (which held the top spot of the Green500 list on July 2013) that combines CPU, GPU and MIC technologies to achieve higher energy efficiency. Our model takes as input workload information - the number and location of resources that are used by each job at a certain time - and calculates the resulting system-level power consumption. When jobs are submitted to the system, the workload configuration can be foreseen based on the scheduler policies, and our model can then be applied to predict the ensuing system-level power consumption. Additionally, alternative workload configurations can be evaluated from a power perspective and more efficient ones can be selected. Applications of the model include not only power-aware scheduling but also prediction of anomalous behavior.
With energy needs becoming a major concern for large computational infrastructures, numerous recent research efforts have focused on reducing power usage. A large amount of work regards modeling power for various types of computing units, starting from load, frequency and other hardware counters. For instance, single and dual core CPU power is modeled in @cite_16 by considering the relation between the probability distribution functions of load and power, while servers with up to 8 cores are studied in @cite_10 @cite_18 . GPU power is estimated from load measures in @cite_2 . These methods do not allow for advance prediction in real life scenarios, since load and hardware counters cannot be known in advance, unless they can be predicted through other methods. Our method is significantly different in that we model total system power starting exclusively from workload measures, without the need to monitor the individual components, enabling .
@cite_18 @cite_16 @cite_10 @cite_2
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Balancing the performance and the energy consumption of the servers is one of the important issues in large-scale computing infrastructure such as data centers. Measuring or accurately estimating power consumption of a server is one of the most fundamental and enabling technologies for enhancing energy efficiency of a server because how the server consumes the supplied power is essential for constructing a power management policy. For the purpose, power models for server systems have been extensively studied. However, most of existing works are too complex to be used real-time, because gathering the data for estimating the power consumption causes much overhead. In this paper, we propose a simple power model for a multicore server. Our model is simple enough to gather only four parameters: operating frequency, the number of active cores, the number of cache accesses and the number of the last level cache misses. We show our model is simple but relatively accurate by experiments that show the model has over 90 accuracy. Quantitatively estimating the relationship between the workload and the corresponding power consumption of a multicore processor is an essential step towards achieving energy proportional computing. Most existing and proposed approaches use Performance Monitoring Counters (Hardware Monitoring Counters) for this task. In this paper we propose a complementary approach that employs the statistics of CPU utilization (workload) only. Hence, we model the workload and the power consumption of a multicore processor as random variables and exploit the monotonicity property of their distribution functions to establish a quantitative relationship between the random variables. We will show that for a single-core processor the relationship is best approximated by a quadratic function whereas for a dualcore processor, the relationship is best approximated by a linear function. We will demonstrate the plausibility of our approach by estimating the power consumption of both custom-made and standard benchmarks (namely, the SPEC power benchmark and the Apache benchmarking tool) for an Intel and AMD processors. Power management is one of the biggest challenges facing current data centers. As processors consume the dominant amount of power in computer systems, power management of multicore processors is extremely significant. An efficient power model that accurately predict the power consumption of a processor is required to develop effective power management techniques. However, this challenge rises with using virtualization and increasing number of cores in the processors. In this paper, we analyze power consumption of a multicore processor, we develop three statistical CPU-Power models based on the number of active cores and average running frequency using a multiple liner regression. Our models are built upon a virtualized server. The models are validated statistically and experimentally. Statistically, our models cover 97 of system variations. Furthermore, we test our models with different workloads and three benchmarks. The results show that our models achieve better performance compared to the recently proposed model for power management in virtualized environments. Our models provide highly accurate predictions for un-sampled combinations of frequency and cores, 95 of the predicted values have less than 7 error. Thus, we can integrate these models into power management mechanisms for a dynamic configuration of a virtual machine in terms of the number of its virtual-CPUs and the frequency of physical cores to achieve both performance and power constrains. In recent years, more and more transistors have been integrated within the GPU, which has resulted in steadily rising power consumption requirements. In this paper we present a preliminary scheme to statistically analyze and model the power consumption of a mainstream GPU (NVidia GeForce 8800gt) by exploiting the innate coupling among power consumption characteristics, runtime performance, and dynamic workloads. Based on the recorded runtime GPU workload signals, our trained statistical model is capable of robustly and accurately predicting power consumption of the target GPU. To the best of our knowledge, this study is the first work that applies statistical analysis to model the power consumption of a mainstream GPU, and its results provide useful insights for future endeavors of building energy-efficient GPU computing paradigms.