Multi-architecture profiler for Android

As a critical transportation infrastructure, with a high flow of people and high-energy consumption in China, coach stations have great potential in energy saving and CO2 emission reduction. In this paper, the building information and energy consumption data of 29 coach stations in five climate regions of China were obtained by field investigations. The annual total comprehensive building energy consumption was 31.37–128.08 kWh/(m2·a). The annual total CO2 emissions from building operation in the coach stations was 17.01–134.77 kgCO2/(m2·a). The heating, ventilation and air conditioning (HVAC) system was the largest energy using and CO2 emissions sector: 30.42–72.47% of the energy consumption and 30.42–83.93% of the CO2 emissions were generated by HVAC system. The energy consumption and CO2 emission level of coach stations and that of other kinds of public buildings were compared. Results showed that the energy consumption and CO2 emission levels of coach stations investigated were relatively low, mainly because the passenger thermal comfort was scarified. Based on the investigation data, energy consumption analysis models of coach stations in five regions were established by simulation when the passenger thermal comfort was met. The potentials of energy saving and CO2 emission reduction were studied from forms of the HVAC system, heat recovery and natural illumination.

Статическая бинарная оптимизация – один из способов ускорить исполняемый файл без исходного кода. Данная технология применяется в бинарном оптимизаторе BOLT (Binary Optimization and Layout Tool), требующий для своей работы профиль с информацией о взятых и информацией о не предсказанных переходах, которую на x86 архитектуре можно получить из аппаратной очереди LBR (Last Branch Record). Эта информация необходима ему для перекомпоновки кода, благодаря которой уменьшается количество промахов по инструкционному кэшу и буферу ассоциативной трансляции инструкций. В результате проделанной оптимизации удалось ускорить работу серверных приложений на 8.0% работающих на x86 архитектурах. На применение данной оптимизации на ARM архитектуре есть ограничения, связанные с частым отсутствием возможности получить профильную информацию с помощью аппаратных средств. В данной статье описываются разработанные методы и инструменты, позволяющие получить профильную информацию с помощью трассы исполнения приложения. Процесс сбора трассы реализован с помощью динамической бинарной инструментации. В статье описан алгоритм восстановления профильной информации с применением модели предсказателя перехода. Также описываются реализованные изменения BOLT для расширенной поддержки ARM архитектуры. В результате работы удалось достичь целевых показателей прироста производительности на синтетических тестах и тестовых наборах. Static binary optimization is one of the ways to speed up an executable file without source code. This technology is used in the BOLT (Binary Optimization and Layout Tool), which requires a profile with information about taken and information about not predicted transitions for its work, which can be obtained from the LBR (Last Branch Record) hardware queue on the x86 architecture. This information is necessary for the BOLT in order to relocate the code, that reduces the number of misses on the instruction cache and the buffer of associative translation of instructions. As a result of the optimization, it was possible to speed up the work of server applications by 8.0% running on x86 architectures. There are limitations on the use of this optimization on the ARM architecture due to the frequent lack of the ability to obtain profile information using hardware. This article describes the developed methods and tools that allow us to obtain profile information using the application execution route. The process of collecting the trace is implemented using dynamic binary instrumentation. The article describes an algorithm for restoring profile information using a branch predictor model. The implemented BOLT changes for extended ARM architecture support are also described. As a result of the work, it was possible to achieve performance growth targets on synthetic tests and benchmarks.

Fault Injection for Linux Device Drivers on x86, MIPS and ARM Architectures

Study of oblique granular impingement flow through CFD-DEM simulations and energy consumption statistics

Recent statistics of energy consumption by Cloud datacenter show the DCs consumes more and more energy each year that created big challenge in Cloud research. IT industry is keenly aware of the need for Green Cloud solutions that save energy consumption in Cloud DCs. A great deal of attention has been paid to minimize energy consumption in cloud datacenter. However, to understand the relationships between running tasks and energy consumed by hardware we need to propose mathematical models of energy consumption. The models of energy consumption can be help as to saving energy. Both researchers aim to proposed mechanism for energy consumption. In this paper, we analyzed the relationships between Cloud system manager and energy consumption. This paper aims at proposing and designing energy consumption models with mechanism of prediction energy.

In buildings, a serious waste of energy exists in HVAC, and refrigeration stations account for the most of energy consumption. Because the coupling exists between devices of refrigeration stations, the traditional evaluating indicator based on independent device can not reflect the energy consumption characteristics and the optimal energy consumption of refrigeration stations toward different load. Drawing lessons from the definition of coefficient of performance of refrigerator, we bring forward the concept of energy efficiency of refrigeration stations, define the expression of energy efficiency and energy consumption of refrigeration stations, give out relation between energy efficiency in different cooling water temperature and variation of load, discuss the trend of energy consumption and energy efficiency of devices of refrigeration stations and the method to improve the energy efficiency. In order to compare the energy efficiency of refrigeration stations in buildings of different style, we bring in rated energy efficiency as parameter to represent the real-time energy efficiency when using actual operation data to calculate the real-time energy efficiency of refrigeration stations in buildings of different style. The energy efficiency can not only reflect both the operating efficiency and the potential of energy-saving of refrigeration stations, but also be used qualitatively for analyzing the base energy consumption in buildings.

In the context of “new normal” economy and frequent “haze”, the strategy of energy conservation and emission reduction aiming to lower costs and reduce pollution is currently still a major strategic direction in China and the world, and will remain so for some time in the future. This paper uses the annual data of West Texas Intermediate (WTI) crude oil price in 1987–2014 as samples. We firstly present the direction and mechanism of the influence of oil price change on total consumption of every kind of energy by path analysis, and then consider establishing a Structural Vector Autoregression model of energy conservation and emission reduction in three statuses. Research shows that if the international oil price increases by 1%, the energy consumption per GDP and carbon dioxide emission increase by 0.092% and 0.053% respectively in the corresponding period. In the status of high energy consumption and high emission, if the international oil price increases by 1%, the energy consumption per GDP and carbon dioxide emission increase by 0.043% and 0.065% respectively in the corresponding period. In the status of low energy consumption and low emission, if the international oil price increases by 1%, the energy consumption per GDP per unit increases by 0.067% and carbon dioxide emission decreases by 0.085% in the corresponding period.

We have developed a user-level dynamic binary instrumentation (DBI) tool on ARM architecture to enable applying various analysis techniques such as performance evaluation, profiling, and bug detection. Most of existing DBI tools are based on the x86 architecture. As demands for the similar approaches mainly performed in x86 are getting higher in an embedded device domain as well, DBI environment is strongly required for embedded devices especially ARM architecture which has a majority of market share in embedded systems. Only a few tools are currently available to ARM but they are very limited to a specific purpose. Therefore, we designed and implemented a general-purpose DBI tool on ARM to execute an arbitrary code between instructions of a target process. Our tool supports native execution environment which is required for security analysis. We have conducted some experiments with common UNIX utilities and an Android application to validate our tool by tracing and saving context information of every instruction of a desired thread. As a result, we have confirmed that it works as expected without affecting the original context of the target process.

Through the real-time monitoring of key cities and buildings across the country, energy consumption statistics, energy consumption auditing, energy efficiency publicity and energy consumption analysis were adopted to realize the management mechanism of energy conservation operation of national large public buildings. However, the operational analysis methods of the energy conservation monitoring platform and its guiding significance for the energy conservation renovation project have not appeared after construction. This study aims at summarizing the problems existing in the operation and use of the energy conservation monitoring platform and proposing the guiding significance of monitoring data analysis for energy conservation reform. This paper introduces the system architecture and main functions of the energy conservation supervision platform of a university in Shenyang. Based on the measured results in operation, the energy consumption data of the campus is statistically analyzed and compared, and the application effect of the energy conservation monitoring platform is analyzed in combination with the energy conservation renovation project.

In the past few years, with the rapid development of heterogeneous computing systems (HCS), the issue of energy consumption has attracted a great deal of attention. How to reduce energy consumption is currently a critical issue in designing HCS. In response to this challenge, many energy-aware scheduling algorithms have been developed primarily using the dynamic voltage-frequency scaling (DVFS) capability which has been incorporated into recent commodity processors. However, these techniques are unsatisfactory in minimizing both schedule length and energy consumption. Furthermore, most algorithms schedule tasks according to their average-case execution times and do not consider task execution times with probability distributions in the real-world. In realizing this, we study the problem of scheduling a bag-of-tasks (BoT) application, made of a collection of independent stochastic tasks with normal distributions of task execution times, on a heterogeneous platform with deadline and energy consumption budget constraints. We build execution time and energy consumption models for stochastic tasks on a single processor. We derive the expected value and variance of schedule length on HCS by Clark's equations. We formulate our stochastic task scheduling problem as a linear programming problem, in which we maximize the weighted probability of combined schedule length and energy consumption metric under deadline and energy consumption budget constraints. We propose a heuristic energy-aware stochastic task scheduling algorithm called ESTS to solve this problem. Our algorithm can achieve high scheduling performance for BoT applications with low time complexity $O(n(M+\log n))$ , where $n$ is the number of tasks and $M$ is the total number of processor frequencies. Our extensive simulations for performance evaluation based on randomly generated stochastic applications and real-world applications clearly demonstrate that our proposed heuristic algorithm can improve the weighted probability that both the deadline and the energy consumption budget constraints can be met, and has the capability of balancing between schedule length and energy consumption.

The COVID-19 pandemic has generated multiple impacts. In particular, in the educational sector, the virtual class modality generated changes in the patterns of energy consumption at the institutional level; the identification of this consumption will allow us to reflect on new energy saving and efficient use strategies. In this research, we present a case study of the effects of the COVID-19 pandemic on electricity consumption in 13 state universities in Michoacán, Mexico. Electric energy consumption has been evaluated before and during the presence of the COVID-19 between 2019 and 2020. The comparative analysis estimated the reduction in energy consumption and its economic and environmental impact. The results show a considerable decrease in electricity consumption, generating an average saving of 76.24 MWh/month, which translates into an annual emission reduction from 2019 to 2020 of approximately 497 TnCO2e, and in economic terms of $8,882.25 USD each month. In general, it was identified that consumption patterns in the use of machinery and computer equipment for administrative activities were drastically reduced. If education continues in virtual or hybrid modes, energy consumption schemes will continue to decline and institutions could move towards resilient, affordable, and sustainable models of energy production and consumption.

The aim of appliance identification methods is to get electrical energy consumption at appliance level based on aggregate measurements from a single energy meter. The appliance identification methods in conjunction with smart meters provide consumers with detailed statistics of electrical energy consumption. The detailed feedback could lead to a reduction in energy consumption, including increased consumer awareness. In this paper we describe electrical parameters and appliance groups with different electrical behaviors. We review different steady and transient state identification methods. Then we compare requirements and limitations of discussed methods.

Performance analysis of rotatable energy system of high-altitude airships in real wind field

The rapid growth of language models, particularly in code generation, requires substantial computational resources, raising concerns about energy consumption and environmental impact. Optimizing language models inference resource utilization is crucial, and Small Language Models (SLMs) offer a promising solution to reduce resource demands. Our goal is to analyze the impact of deep learning serving configurations, defined as combinations of runtime engines and execution providers, on resource utilization, in terms of energy consumption, execution time, and computing-resource utilization from the point of view of software engineers conducting inference in the context of code generation SLMs. We conducted a technology-oriented, multi-stage experimental pipeline using twelve code generation SLMs to investigate energy consumption, execution time, and computing-resource utilization across the configurations. Significant differences emerged across configurations. CUDA execution provider configurations outperformed CPU execution provider configurations in both energy consumption and execution time. Among the configurations, TORCH paired with CUDA demonstrated the greatest energy efficiency, achieving energy savings from 37.99% up to 89.16% compared to other serving configurations. Similarly, optimized runtime engines like ONNX with the CPU execution provider achieved from 8.98% up to 72.04% energy savings within CPU-based configurations. Also, TORCH paired with CUDA exhibited efficient computing-resource utilization. Serving configuration choice significantly impacts resource utilization. While further research is needed, we recommend the above configurations best suited to software engineers’ requirements for enhancing serving resource utilization efficiency. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board . • Actionable guidelines to improve resource utilization of deep learning serving. • Measured impact of deep learning serving configurations on resource utilization. • A reproducible pipeline and analysis of deep learning serving configurations.

The use of spare tiles in a networks-on-chip based multi-processor chip can improve the yield, reducing the cost of the chip and maintaining the system functionality even if the chip is defective. However, the impact of this approach on application characteristics, such as energy consumption and execution time, is not documented. For instance, on one hand the application tasks might be mapped onto any tile of a defect-free chip. On the other hand, a chip with a defective tile needs special task mapping that avoid fault tiles. This paper presents a task mapping aware of faulty tiles, where an alternative task mapping can be generated and evaluated in terms of energy consumption and execution time. The results show that faults on tiles have, on average, a small effect on energy consumption but no significant effect on execution time. It demonstrates that spare tiles can improve yield with a small impact on the application requirements.