Energy-efficient multithreading for a hierarchical heterogeneous multicore through locality-cognizant thread generation

Energy costs have become increasingly problematic for high performance processors, but the rising number of cores on-chip offers promising opportunities for energy reduction. Further, emerging architectures such as heterogeneous multicores present new opportunities for improved energy efficiency. While previous work has presented novel memory architectures, multithreading techniques, and data mapping strategies for reducing energy, consideration to thread generation mechanisms that take into account data locality for this purpose has been limited. This study presents methodologies for the joint partitioning of data and threads to parallelize sequential codes across an innovative heterogeneous multicore processor called the Passive/Active Multicore (PAM) for reducing energy consumption from on-chip data transport and cache access components while also improving execution time. Experimental results show that the design with automatic thread partitioning offered reductions in energy-delay product (EDP) of up to 48%.     

Popularity-based covering sets for energy proportionality in shared-nothing clusters

Energy management for large-scale clusters has been the subject of significant research attention in recent years. The principle of energy proportionality states that we can save energy by activating only a subset of cluster nodes, in proportion to the current load. However, achieving the energy proportionality in shared-nothing clusters is challenging, because the arbitrary deactivation of nodes would make some data become unavailable. In this paper, we propose a new algorithm, named popularity-based covering sets (PCS), to achieve the energy proportionality in large-scale shared-nothing clusters. PCS determines the set of active nodes dynamically, in order to achieve the design goals of (a) guaranteeing the minimum level of availability for every data so that any job can execute promptly, and (b) providing more replicas for popular data to mitigate contention on the data. This differs from previous studies, where some data may become unavailable, or they provide the same number of replicas for every data. Furthermore, PCS is rack-aware and thus it can reduce the energy consumption of power-hungry rack components. Experiment results indicate that PCS improves the overall energy savings by up to 62% compared to previous algorithms without significant performance loss.     

Governing energy consumption in Hadoop through CPU frequency scaling: An analysis

With increasingly inexpensive storage and growing processing power, the cloud has rapidly become the environment of choice to store and analyze data for a variety of applications. Most large-scale data computations in the cloud heavily rely on the MapReduce paradigm and on its Hadoop implementation. Nevertheless, this exponential growth in popularity has significantly impacted power consumption in cloud infrastructures. In this paper, we focus on MapReduce processing and we investigate the impact of dynamically scaling the frequency of compute nodes on the performance and energy consumption of a Hadoop cluster. To this end, a series of experiments are conducted to explore the implications of Dynamic Voltage and Frequency Scaling (DVFS) settings on power consumption in Hadoop clusters. By enabling various existing DVFS governors (i.e., performance, powersave, ondemand, conservative and userspace) in a Hadoop cluster, we observe significant variation in performance and power consumption across different applications: the different DVFS settings are only sub-optimal for several representative MapReduce applications. Furthermore, our results reveal that the current CPU governors do not exactly reflect their design goal and may even become ineffective to manage the power consumption in Hadoop clusters. This study aims at providing a clearer understanding of the interplay between performance and power management in Hadoop clusters and therefore offers useful insight into designing power-aware techniques for Hadoop systems.     

iPACS: Power-aware covering sets for energy proportionality and performance in data parallel computing clusters

Energy consumption in datacenters has recently become a major concern due to the rising operational costs and scalability issues. Recent solutions to this problem propose the principle of energy proportionality, i.e., the amount of energy consumed by the server nodes must be proportional to the amount of work performed. For data parallelism and fault tolerance purposes, most common file systems used in MapReduce-type clusters maintain a set of replicas for each data block. A covering subset is a group of nodes that together contain at least one replica of the data blocks needed for performing computing tasks. In this work, we develop and analyze algorithms to maintain energy proportionality by discovering a covering subset that minimizes energy consumption while placing the remaining nodes in low-power standby mode in a data parallel computing cluster. Our algorithms can also discover covering subset in heterogeneous   computing environments. In order to allow more data parallelism, we generalize our algorithms so that it can discover k$k$-covering subset, i.e., a set of nodes that contain at least k$k$ replicas of the data blocks. Our experimental results show that we can achieve substantial energy saving without significant performance loss in diverse cluster configurations and working environments.     

Eco-Physic: Eco-Physical design initiative for very large databases

In the Big Data Era, the management of energy consumption by servers and data centers has become a challenging issue for companies, institutions, and countries. In data-centric applications, Database Management Systems are one of the major energy consumers when executing complex queries involving very large databases. Several initiatives have been proposed to deal with this issue, covering both the hardware and software dimensions. They can be classified in two main approaches assuming that either (a) the database is already deployed on a given platform, or (b) it is not yet deployed. In this study, we focus on the first set of initiatives with a particular interest in physical design, where optimization structures (e.g., indexes, materialized views) are selected to satisfy a given set of non-functional requirements such as query performance for a given workload. In this paper, we first propose an initiative, called Eco-Physic, which integrates the energy dimension into the physical design when selecting materialized views, one of the redundant optimization structures. Secondly, we provide a multi-objective formalization of the materialized view selection problem, considering two non-functional requirements: query performance and energy consumption while executing a given workload. Thirdly, an evolutionary algorithm is developed to solve the problem. This algorithm differs from the existing ones by being interactive, so that database administrators can adjust some energy sensitive parameters at the final stage of the algorithm execution according to their specifications. Finally, intensive experiments are conducted using our mathematical cost model and a real device for energy measurements. Results underscore the value of our approach as an effective way to save energy while optimizing queries through materialized views structures.     

How “OPTIMUS” is a city in terms of energy optimization? e-SCEAF: A web based decision support tool for local authorities

Nowadays cities tend to become “Smarter”, usually disregarding the issues of energy efficiency and sustainability. Therefore, optimizing energy use in a city remains a challenge and respective decision support systems are important to guide local authorities toward that direction. This paper provides a holistic approach presenting a Smart City Energy Assessment Framework (SCEAF) along with a specific web based decision support tool, the so-called e-SCEAF, which can provide local authorities with fruitful results for assessing the energy behavior and performance of their city. The tool merges heterogeneous information, such as clearly quantifiable energy related indicators, the related city policy context performance and the integration of smart infrastructure. This multi-source information fusion is based on the 2-tuple linguistic representation model of Herrera and Martínez. This particular model has been widely used in decision problems and was mainly selected due to the fact that it provides linguistic results that are accurate and easy to understand by the cities’ local authorities. The performance, usefulness and effectiveness of the SCEAF framework and the e-SCEAF tool are tested on a real life application in three different cities, Savona (Italy), Sant Cugat del Vallès (Spain) and Zaanstad (The Netherlands). In this respect, the role of fusion methods and algorithms for merging multiple information will be evaluated in a “real life environment”.     

Bond graph modeling from an object oriented modeling point of view

Along with an ever increasing model complexity, a so-called object oriented approach to physical systems modeling has become more and more popular throughout the last few years. Frequently used keywords are multi-domain modeling, model reuse, and non-causal equations. On the other hand the physical systems modeling methodology based on bond graphs has been in use worldwide since Paynter devised bond graphs more than 35 years ago. It seems that due to different roots and a different terminology, aspects of one of the two approaches are not fully appreciated by those who adhere to the other modeling paradigm. By relating features of object-oriented modeling (OOM) to corresponding ones of the older bond graph methodology, it is pointed out what both modeling approaches have in common and what is different. As a working modeling language, Modelica is used since it seems that this object oriented modeling language is going to receive an increasing attention as a neutral exchange format between proprietary modeling tools. As an application example that combines the electrical, the hydraulic and the mechanical energy domain in a single system, a hydraulic drive with a controlled displacement pump is considered.     

Generic Markov model of the contention access period of IEEE 802.15.4 MAC layer

The IEEE-802.15.4 standard is poised to become the global standard for low data rate, low energy consumption Wireless Sensor Networks. By assigning the same sets of contention access parameters for all data frames and nodes, the Contention Access Period (CAP) of the slotted IEEE-802.15.4 currently provides an even channel access functionality and no service differentiation. However, some applications may require service differentiation and traffic prioritization support to accommodate high-priority traffic (e.g., alarms). In order to simulate a scenario in which different sets of access parameters for different node classes can be configured, this paper develops a Markov-chain-based model of the CAP of the IEEE-802.15.4-MAC. Our Markov model can be used to evaluate the impact of mixing node classes in important factors like the throughput, energy consumption, probability of delivery and the packet latency. The model has been used to provide traffic differentiation in a high saturation scenario in which a set of nodes can be configured to increase 76% the probability of sending a packet and reduce 58% latency, with a 69% energy penalty, in comparison with a standard scenario. The accuracy of the Markov model is validated by extensive ns-2 simulations.     

Multi-layer-based opportunistic data collection in mobile crowdsourcing networks

Along with the explosive popularity of wireless mobile devices and availability of high data rates, new crowdsourcing paradigms have emerged to leverage the power of problem-solving by crowds. A crucial challenge in crowdsourcing is data collection. With the increasing number of mobile users, device to device communication with opportunistic connections has become a real possibility, reducing the load on infrastructure based networks. Crowdsourcing over such opportunistic links presents with unique challenges. This paper proposes to exploit opportunistic transmission to collect data in crowdsourced networks, by using multiple layers of social graphs along with weight training for energy efficient data collection. We design two types of multi-layer-based opportunistic data collection methods by using different dimensions of data. Simulation experiments show that using these techniques, delivery ratio can be increased while reducing the load and energy consumption of the mobile network.     

Turbulence modification by periodically modulated scale-dependent forcing

The response of turbulent flow to time-modulated forcing is studied by direct numerical simulation of the Navier-Stokes equations. The forcing is modulated via periodic energy-input variations at a frequency ω. Harmonically modulated forcing of the large scales is shown to yield a response maximum at frequencies in the range of the inverse of the large-eddy turnover time, as well as a characteristic rapid change of the phase-angle between forcing and response. Harmonically modulated broadband forcing is also studied in case a wide spectrum of length-scales is forced simultaneously. If smaller length-scales are also explicitly agitated by the forcing, the response maximum is found to occur at higher frequencies and to become less pronounced. In case the explicitly forced spectrum is sufficiently wide, a response maximum was not observed. At high modulation frequencies the amplitude of the kinetic energy response decreases as 1/ω, consistent with theoretical predictions. The amplitude response to intense pulses of energy injected via a square-wave modulated forcing at the largest scales was also studied. This forcing protocol induces a more complicated response structure that also displays a maximum in the kinetic energy amplitude response at a modulation frequency comparable to the harmonically modulated case.     

A C° continuous linear beam/bilinear plate flexure element

Design of a simple C°-continuous beam/plate flexure element based on a shear-deformable theory is attempted. Emphasis is placed on development of a low order linear/bilinear element. However, past experience shows that such elements become very “stiff” especially when thickness is reduced (conforming to Kirchhoff mode). This phenomenon is called “locking.” Attempts have been made in the last several years by a few investigators to overcome this problem.The locking problem is resolved here through a new approach. The total strain energy is split into bending and shear energies and an antiparameter in the shear energy term is introduced to avoid locking. By numerical experimentation on beam and plate problems it is shown that the present approach gives good results in the thin limit. It is also shown that the additional/spurious zero energy modes do not arise here because reduced/selective integration is avoided.     

Modeling China's energy consumption behavior and changes in energy intensity

China's demand for energy has grown to fuel its rapidly expanding industrial, commercial and consumer sectors. At the same time, China has become the second largest consumer of petroleum products having surpassed Japan for the first time in 2003. The environmental consequences of a continuation of these trends will have global implications. Government policies and consumers have become more environmentally aware, but the ability of governments to formulate policies has been hindered by the lack of data on inter-factor and inter-fuel substitution possibilities. In this paper Allen partial elasticities of factor and energy substitution, and price elasticities of energy demand are calculated for China's industrial economy using a two-stage translog cost function approach for the period 1995–2004. The results suggest that energy is substitutable with both capital and labor. Coal is significantly substitutable with electricity and slightly complementary with oil, while oil and electricity are slightly substitutable. China's energy intensity is increasing during the study period and the major driver appears to be due to the increased use of energy-intensive technology.     

Temperature prediction of rolling tires by computer simulation

A numerical procedure has been applied for investigating the temperature distribution in a smooth tread bias tire of a light truck, operated under different speeds, pneumatic pressures, and loading conditions. Prior to simulation by the finite element analysis, two separate sets of testing, namely dynamic mechanical testing and material testing, have been conducted in relation to the evaluation of hysteresis (H) and total strain energy (U_sed), respectively. Hysteresis loss energy is given as (H × U_sed) and considered to relate directly to heat generation rate. Temperature rise is assumed to be due to the energy dissipation from periodic deformation. This dissipation of energy may be equated to be the primary heat generation source. Hysteresis energy loss is used as a bridge to link the strain energy density to the heat source in rolling tires; temperature distribution of rolling tires may be obtained by the steady-state thermal analysis. The above procedure has been shown to facilitate the simulation of the temperature distribution in the rolling tire.An efficient computational process is being introduced to decrease the time for coupled 3D dynamic rolling simulation of tire. Temperature rise under different conditions is discussed with reference to the results of other published studies.     

Finite-difference solution to the Schrodinger equation for the helium isoelectronic sequence

The method described previously for the solution of the Schrodinger equation for S-type states of helium has been applied to the helium isoelectronic sequence. The Schrodinger equation, which is an elliptic partial-differential equation, is converted to a set of finite-difference equations which are solved by a relaxed iterative technique. The method is applied to the 1¹S two-electron systems for Z = 1 through 8 and 2³S state for Z = 3. The results include expectation values of the total energy, kinetic and potential terms, and rⁿ, n = ?2, ?1, 1, 2, and are compared with the work of Pekeris. The total energy for the hydrogen ion, which has only one bound state, is ?0.52746 and differs from Pekeris' value by 0.055%. Other total energy values differ by 0.004% or less.     

一种能量感知的无线传感网拓扑控制算法

本文为不平衡能量分布的异构无线传感网构建一种拓扑控制算法EADCA。在该算法中,每个节点根据自己的剩余能量和邻居节点的平均剩余能量计算簇头声明报文发送的理论时刻;在该理论时刻,没收到任何簇头声明报文的节点成为簇头,该簇头广播簇头声明报文;收到簇头声明报文的节点成为普通节点并放弃发送簇头声明报文。同时,该算法在簇头竞争过程中使用经验数据,并对孤立节点和能量过低节点进行休眠。仿真结果表明,EADCA能够延长网络生命周期,有效控制簇头分布密度。     

From chaining blocks to breaking even: A study on the profitability of bitcoin mining from 2012 to 2016

Bitcoin is a widely-spread payment instrument, but it is doubtful whether the proof-of-work (PoW) nature of the system is financially sustainable on the long term. To assess sustainability, we focus on the bitcoin miners as they play an important role in the proof-of-work consensus mechanism of bitcoin to create trust in the currency. Miners offer their services against a reward while recurring expenses. Our results show that bitcoin mining has become less profitable over time to the extent that profits seem to converge to zero. This is what economic theory predicts for a competitive market that has a single homogenous good. We analyze the actors involved in the bitcoin system as well as the value flows between these actors using the e³value methodology. The value flows are quantified using publicly available data about the bitcoin network. However, two important value flows for the miners, namely hardware investments and expenses for electricity power, are not available from public sources. Therefore, we contribute an approach to estimate the installed base of bitcoin hardware equipment over time. Using this estimate, we can calculate the expenses miner should have. At the end of our analysis period, the marginal profit of mining a bitcoin becomes negative, i.e., to a loss for the miners. This loss is caused by the consensus mechanism of the bitcoin protocol, which requires a substantial investment in hardware and significant recurring daily expenses for energy. Therefore, a sustainable crypto currency needs higher payments for miners or more energy efficient algorithms to achieve consensus in a network about the truth of the distributed ledger.     

On design optimization for structural crashworthiness and its state of the art

Optimization for structural crashworthiness and energy absorption has become an important topic of research attributable to its proven benefits to public safety and social economy. This paper provides a comprehensive review of the important studies on design optimization for structural crashworthiness and energy absorption. First, the design criteria used in crashworthiness and energy absorption are reviewed and the surrogate modeling to evaluate these criteria is discussed. Second, multiobjective optimization, optimization under uncertainties and topology optimization are reviewed from concepts, algorithms to applications in relation to crashworthiness. Third, the crashworthy structures are summarized, from generically novel structural configurations to industrial applications. Finally, some conclusions and recommendations are provided to enable academia and industry to become more aware of the available capabilities and recent developments in design optimization for structural crashworthiness and energy absorption.

     

Increasing the energy efficiency of microcontroller platforms with low-design margin co-processors

Reducing the energy consumption in low cost, performance-constrained microcontroller units (MCU’s) cannot be achieved with complex energy minimization techniques (i.e. fine-grained DVFS, Thermal Management, etc), due to their high overheads. To this end, we propose an energy-efficient, multi-core architecture combining two homogeneous cores with different design margins. One is a performance-guaranteed core, also called Heavy Core (HC), fabricated with a worst-case design margin. The other is a low-power core, called Light Core (LC), which has only a typical-corner design margin. Post-silicon measurements show that the Light core has a 30% lower power density compared to the Heavy core, with only a small loss in reliability. Furthermore, we derive the energy-optimal workload distribution and propose a runtime environment for Heavy/Light MCU platforms. The runtime decreases the overall energy by exploiting available parallelism to minimize the platform’s active time. Results show that, depending on the core to peripherals power-ratio and the Light core’s operating frequency, the expected energy savings range from 10 to 20%.     

The role of free work identity in viscous compressible flows

Some consequences of energy identity are discussed, on assumption that there exists a neighborhood of S_b of radius η where the total energy is a minimum. For fluid phase transition the neighborhood where the rest state S_b results in isolated minimum for internal energy has finite radius r that will restrict to zero as basic density ϱ_b approaches a critical value ϱ_*. Nonlinear asymptotic stability for barotropic viscous fluids is proved by use of free work identity which enables us to provide a stronger generalized energy inequality. The stability theorem is proved in a class of regular unsteady flows which are supposed to exist. Nonlinear instability for fluid phase change with zero external forces is proved. The goal is reached assuming by absurdum that ϱ is stable in L^∞ norm.     

Expressing embedded systems configurations at high abstraction levels with UML MARTE profile: Advantages,limitations and alternatives

Embedded systems have become an essential aspect of our professional and personal lives. From avionics, transport and telecommunication systems to general commercial appliances such as smart phones, high definition TVs and gaming consoles; it is difficult to find a domain where these systems have not made their mark. Moreover, Systems-on-Chips (SoCs) which are considered as an integral solution for designing embedded systems, offer advantages such as run-time reconfiguration that can change system configurations during execution, depending upon Quality-of-Service (QoS) criteria such as performance and energy levels. This article deals with aspects related to modeling of these configurations, useful for describing various states of an embedded system, from both structural and operational viewpoints. Our proposal adapts a high abstraction level approach based on the principles of Model-Driven Engineering (MDE) and takes into account the UML MARTE profile for modeling of real-time and embedded systems. Elevating the design abstraction levels help to increase design productivity and achieve execution platform independence, among other advantages. The article details the current proposition of configurations in MARTE via some examples, and points out the advantages as well as some limitations, mainly concerning the semantic aspects of the defined concepts. Finally, we report our experiences on the modeling of an alternate notion of configurations and execution modes within the MARTE compliant Gaspard2 SoC Co-Design framework that has been successful for the design as well as implementation of FPGA based SoCs.