Power Management Techniques for Data Centers: A Survey

With growing use of internet and exponential growth in amount of data to be stored and processed (known as 'big data'), the size of data centers has greatly increased. This, however, has resulted in significant increase in the power consumption of the data centers. For this reason, managing power consumption of data centers has become essential. In this paper, we highlight the need of achieving energy efficiency in data centers and survey several recent architectural techniques designed for power management of data centers. We also present a classification of these techniques based on their characteristics. This paper aims to provide insights into the techniques for improving energy efficiency of data centers and encourage the designers to invent novel solutions for managing the large power dissipation of data centers.Comment: Keywords: Data Centers, Power Management, Low-power Design, Energy Efficiency, Green Computing, DVFS, Server Consolidatio

Towards Energy Efficiency in Heterogeneous Processors: Findings on Virtual Screening Methods

The integration of the latest breakthroughs in computational modeling and high performance computing (HPC) has leveraged advances in the fields of healthcare and drug discovery, among others. By integrating all these developments together, scientists are creating new exciting personal therapeutic strategies for living longer that were unimaginable not that long ago. However, we are witnessing the biggest revolution in HPC in the last decade. Several graphics processing unit architectures have established their niche in the HPC arena but at the expense of an excessive power and heat. A solution for this important problem is based on heterogeneity. In this paper, we analyze power consumption on heterogeneous systems, benchmarking a bioinformatics kernel within the framework of virtual screening methods. Cores and frequencies are tuned to further improve the performance or energy efficiency on those architectures. Our experimental results show that targeted low‐cost systems are the lowest power consumption platforms, although the most energy efficient platform and the best suited for performance improvement is the Kepler GK110 graphics processing unit from Nvidia by using compute unified device architecture. Finally, the open computing language version of virtual screening shows a remarkable performance penalty compared with its compute unified device architecture counterpart.Ingeniería, Industria y Construcció

SQUASH: Simple QoS-Aware High-Performance Memory Scheduler for Heterogeneous Systems with Hardware Accelerators

Modern SoCs integrate multiple CPU cores and Hardware Accelerators (HWAs) that share the same main memory system, causing interference among memory requests from different agents. The result of this interference, if not controlled well, is missed deadlines for HWAs and low CPU performance. State-of-the-art mechanisms designed for CPU-GPU systems strive to meet a target frame rate for GPUs by prioritizing the GPU close to the time when it has to complete a frame. We observe two major problems when such an approach is adapted to a heterogeneous CPU-HWA system. First, HWAs miss deadlines because they are prioritized only close to their deadlines. Second, such an approach does not consider the diverse memory access characteristics of different applications running on CPUs and HWAs, leading to low performance for latency-sensitive CPU applications and deadline misses for some HWAs, including GPUs. In this paper, we propose a Simple Quality of service Aware memory Scheduler for Heterogeneous systems (SQUASH), that overcomes these problems using three key ideas, with the goal of meeting deadlines of HWAs while providing high CPU performance. First, SQUASH prioritizes a HWA when it is not on track to meet its deadline any time during a deadline period. Second, SQUASH prioritizes HWAs over memory-intensive CPU applications based on the observation that the performance of memory-intensive applications is not sensitive to memory latency. Third, SQUASH treats short-deadline HWAs differently as they are more likely to miss their deadlines and schedules their requests based on worst-case memory access time estimates. Extensive evaluations across a wide variety of different workloads and systems show that SQUASH achieves significantly better CPU performance than the best previous scheduler while always meeting the deadlines for all HWAs, including GPUs, thereby largely improving frame rates

Achieving Accurate Predictions of Future Events Under Hardware Heterogeneity

Heterogeneous hardware is becoming increasingly available in modern hardware, while research breakthroughs enforce the expectation that heterogeneity will keep increasing in the future. Significant gains can be achieved via appropriate utilization of heterogeneity, in terms of performance and power consumption, however, poor utilization can have a detrimental effect. Intelligent scheduling and resource management is a crucial challenge we need to overcome in order to harvest the full potential of heterogeneous hardware. As systems become larger and include greater levels of hardware diversity, the importance of intelligent scheduling and resource management is further accentuated.This dissertation presents techniques that aid the process of scheduling and resource management in the presence of heterogeneous hardware, via accurately predicting upcoming runtime events. With a proactive and accurate view of the near future, schedulers can utilize the underlying hardware more efficiently, and fully take advantage of the available benefits.By adapting a majority element heuristic, this dissertation significantly improves the accuracy of predicting memory addresses about to be accessed, while reducing prediction-related costs by a factor of ten thousand compared to previously proposed predictive approaches. Coupled with novel microarchitectural modifications, accurate address predictions are shown to improve the performance of heterogeneous memory architectures.Machine learning-based performance predictors are further presented, capable of predicting a program's performance when executed on a given general-purpose core. Trained to model the subtleties of the interaction between hardware and software, these predictors are capable of generating highly accurate predictions even for cores with varied Instruction Set Architectures. Utilizing these performance predictions for job scheduling, is shown to improve overall system performance. The trained predictors are further examined and interpreted in order to visualize the correlations between features picked up and amplified during training.Finally, this dissertation demonstrates that scheduling algorithms cannot guarantee deriving an optimal schedule during realistic execution scenarios due to the underlying hardware heterogeneity, the wide range of runtime requirements of software, as well as prediction error from performance predictors. In response, deep neural networks are trained to select one scheduling approach from a list of options with varied overheads and correctness guarantees. The scheduling approach chosen, is the one which will most likely return the highest-performance schedule with the lowest overhead, given a particular instance of the job-to-core assignment problem

Using GDELT Data to Evaluate the Confidence on the Spanish Government Energy Policy

The growing demand for affordable, reliable, domestically sourced, and low-carbon electricity is a matter of concern and it is driven by several causes including public policy priorities. Policy objectives and new technologies are changing wholesale market design. The analysis of different aspects of energy markets is increasingly on the agendas of academics, firms’ managers or policy makers. Some concerns are global and are related to the evolution of climate change phenomena. Others are regional or national and they strongly appear in countries like Spain with a high dependence on foreign energy sources and high potential of domestic renewable energy sources. We can find a relevant case in Spanish solar energy policy. A series of regulatory reforms since 2010 reduce revenues to existing renewable power generators and they end up the previous system of support to new renewable generation. This policy change has altered the composition of the energy market affecting investment decisions. In this paper, we analyze the public opinion about energy policy of the Spanish Government using the Global Database of Events, Language, and Tone (GDELT). The GDELT Project consists of over a quarter-billion event records in over 300 categories covering the entire world from 1979 to present, along with a massive network diagram connecting every person, organization, location, and theme to this event database. Our aim is to build sentiment indicators arising from this source of information and, in a final step, evaluate if positive and negative indexes have any effect on the evolution of key market variables as prices and demand