Empirical analysis of Python’s energy impact: Evidence from real measurements

This paper studied the characteristics of time and energy consumption of gear hobbing in batch production of small modulus gear. A method of integrating design of experiment, response surface method and multi-objective salp swarm algorithm (DOE/RSM/MMSA) is proposed for the complex optimization of machining parameters (namely hob rotation speed, cutting feed and depth of cut) in gear hobbing process. This paper uses three-level factorial design method to design gear hobbing experiments, and carries out mathematical and statistical analysis on the experimental results. It is found that cutting feed is the most significant machining parameter followed by hob rotation speed and depth of cut to reduce processing time and energy consumption. The prediction model of processing time and energy consumption in gear hobbing process is obtained through response surface methodology. The relative error rates of the predicted value and the actual value of the processing time and energy consumption are 0.11% and 0.09% respectively, indicating the validity of the model. Multi-objective salp swarm algorithm is used to optimize the machining parameters to minimizing processing time and energy consumption. Finally, through the comparison with the existing research results, it is concluded that the optimized machining parameters have better processing effect to achieve minimized processing time and energy consumption, which shows the effectiveness and rationality of the method proposed, and provides references for the decision-making of machining parameters in workshop.

An integrated approach to investigate the energy consumption for manufacturing and surface finishing 3D printed Inconel 718 parts

Measuring the energy spent by a software application is a problem that can be solved by having the proper hardware or software instruments. However, not always such tools are available or the provide resolution cannot fit the needs of the user, for instance when measuring a very small piece of code. This problem is particularly relevant on mobile software products, as they are developed to be executed in an environment limited in energy resources. Mobile software engineers should take special consideration on the energy consumption when designing and implementing an application. In this paper, we propose that the energy consumed by a unit of code can be approximated by the execution time. Using software benchmarks run with different data loads, we measured the execution time required to complete the job, and using a software tool to measure the energy spent during the execution of the benchmark, with the objective of finding a relationship among them. We observed that, regardless of the software benchmark, the data load injected and the programming language, of implementation the ratio between the execution time and the energy consumption remains consistent, opening the opportunity to develop techniques to approximate the energy consumption of mobile software based on execution time measurements.

Recent CPU widely utilizes cache memory or cache to decrease access speed gap between the CPU and the main memory. However, the cache occupies a large part of the processor's energy consumption. Furthermore, due to the characteristics of SRAMs, it is hard to reduce the supply voltage of the cache compared to that of the CPU core. Thus, dynamic voltage and frequency scaling (DVFS) which is widely used to reduce the processor's energy consumption does not reduce cache energy significantly. To confront this problem, we investigated a method which applies different power and speed design caches and switching them in proportion to the DVFS activity. Our proposal modifies the L1 cache hierarchy to L1 low-speed cache and L1 high-speed cache and selects them or access in sequential in proportion to DVFS activity to reduce cache energy demand. We confirmed that the proposal has a potential for further reduction of cache energy consumption with both cache design simulation and processor architecture level simulation.

Energy consumption of software has been becoming increasingly significant, since it can vary according to how the software has been developed. In recent years, developers and researchers have been interested in analyzing, among other things, how energy consumption evolves when changes occur from one version to another in any given software. Thus far, the only studies available are theoretical papers that reinforce the idea that maintainability may have an influence on energy use, but this needs to be proven empirically, which is the goal of this article. This work presents an empirical study carried out to test whether there is a relationship between the energy consumption and the maintainability of several versions of Redmine. Maintainability has been assessed by means of different measures, such as the number of lines of code, or the complexity of the software, calculated using SonarCloud, and the energy consumption measurements have been captured using the EET device. The results obtained show that the number of lines of code affects both the energy consumption of the processor and the total consumption of the computer where the software is run. It is intended that the results from this work should serve as a basis for the undertaking of new empirical studies which will enable the relationship between the software maintainability and the energy efficiency of that software to be better understood.

Microalgae are a promising biomass for the biobased economy to produce food, feed, fuel, chemicals and materials. So far, large-scale production of algae is limited and as a result estimates on the performance of such large systems are scarce. There is a need to estimate large-scale biomass productivity and energy consumption, while considering the uncertainty and complexity in such large-scale systems. In this thesis frameworks are developed to assess 1) the productivity during algae cultivation, 2) energy consumption during the transport of resources and processing biomass to biodiesel, and 3) the frameworks are applied to estimate the impact of algae cultivation in the production of algae-based food commodities. Design, location and future scenario are applied to deal with the complexity and uncertainty arising in the various data and models used. The first part of this thesis focuses on the development of a productivity framework for biomass production for flat panels (Chapter 2), horizontal and vertical tubular photobioreactors (Chapter 3) and raceway ponds (Chapter 4). The framework uses bio-physics-based models to simulate the light input on the reactor surface and the light gradient inside the reactor systems. The internal light gradient depends on the reactor geometry and dimensions, and the penetration of diffuse light between parallel reactors, which includes the canyon effect, and the reflection of light from the ground surface to the reactors are incorporated as well. Specific growth rates are derived from this internal light gradient based on species-specific growth characteristics. In raceway ponds the effect of the dynamic water temperature on the specific growth rate is included. The productivity framework enables to study cultivation under a wide range of process conditions and reactor designs, even those which have not been yet developed or tested under outdoor conditions. The results show that regional weather conditions, solar angles and algae species are key factors in making the best choice for the specific reactor design. The productivity framework allows to optimise the reactor design (e.g. geometry, light path, distances between parallel units and height) to the regional light conditions and growth characteristics of the algae species of interest. The best biomass concentration for cultivation varies between the reactor design, location and algae species. We recommend to select species suited to growth well at the regional light angles and weather conditions. An initial global sensitivity analysis shows that the absorption coefficient, maximum specific growth rate and functional cross section of the photosynthetic apparatus are the essential parameters of the model for single flat panels. An important next step is to validate and calibrate the productivity framework using data from outdoor experiments in various reactor designs, at different locations and with several algae species. Algae production is strongly connected to regional weather conditions, but also to the infrastructure for resource supply and to the processing of biomass. The energy consumption for resource supply has not been quantified yet and the energy consumption of biomass processing is mostly based on fixed values. These elements are tackled in part 2 of this thesis. In Chapter 5the productivity framework is combined with logistic models to optimise the supply network for algae cultivation. The results show that the availability, supply and demand of resources has a dominant effect on the feasibility of regions for algae cultivation. Not all locations achieve a positive energy balance for transport and the supply logistics is essential for planning algae cultivation locations. In the Benelux many locations are feasible for algae production due to the availability of large amounts of resources, while the limited supply of CO2in southern France and the Sahara demands for plants which are scattered over the regions. For the Sahara the distance for water transport should be minimal. Still, the average transport distances are higher than commonly assumed and algae cultivation does not necessarily need to take place in proximity of CO2supply. The transport energy consumption is found to be low compared to the energy contained in algae biomass (mostly below 3%). Chapter 6 describesa model-based combinatorial optimisation approach for the energy-efficientprocessing of algae biomass. In this approach, mass and energy balances and additional relations are used to relate the product yield and energy consumption of process units and process routes to the processing conditions. Process routes with the highest net energy ratios are derived by optimising the process conditions of each process unit in a given superstructure. This optimisation leads to 5-38% improvement of the net energy ratio compared to fixed process conditions. The approach moreover allows a bottleneck analysis for each process route. The results show that process design should be tailor-made. The model-based approach proves to be a versatile tool for the design of efficient microalgae processing systems. The developed frameworks combined with scenario studies are a powerful tool to assess algae production. The presented approaches help to reduce the uncertainty in the interpretation of data and are thereby an appropriate basis to use in impact analysis. In Chapter 7this is illustrated for the production of algae protein and oil as food commodities. The design scenarios show the implications of various reactor designs, two algae species and at two locations on biomass productivity, production cost and environmental life cycle indicators. The achievements of this work and the new horizons from this work are discussed in Chapter 8. The results of the developed frameworks demonstrate the power of the scenario approach and show that sensible predictions and projections of biomass productivity and energy consumption for logistics and biomass processing follow from the models.

In recent decades, workshop scheduling has excessively focused on time-related indicators, while ignoring environmental metrics. With the advent of sustainable manufacturing, the energy-aware scheduling problem has been attracting more and more attention from scholars and researchers. In this study, we investigate an energy-aware flexible job shop scheduling problem to reduce the total energy consumption in the workshop. For the considered problem, the energy consumption model is first built to formulate the energy consumption, such as processing energy consumption, idle energy consumption, setup energy consumption and common energy consumption. Then, a mathematical model is established with the criterion to minimize the total energy consumption. Secondly, a modified migrating birds optimization (MMBO) algorithm is proposed to solve the model. In the proposed MMBO, a population initialization scheme is presented to ensure the initial solutions with a certain quality and diversity. Five neighborhood structures are employed to create neighborhood solutions according to the characteristics of the problem. Furthermore, both a local search method and an aging-based re-initialization mechanism are developed to avoid premature convergence. Finally, the experimental results validate that the proposed algorithm is effective for the problem under study.

The EU Green Deal mirrors the societal concern regarding climate change. In the software industry this means reducing the energy consumption of a program. This can be done by looking both at the hardware and the software used. We take a software approach and focus on the programming language choices and how a computer program is written. The programming languages we investigate are Java, JavaScript, Python, PHP, Ruby, C, C++ and C#. We compare programs from The Computer Language Benchmarks Game and these programs are divided in groups that solve the same problem.We find that the choice of programming language and the way of writing your program influences the energy consumption. The programming languages that consume the least amount of energy over all the problems are C and C++, albeit these programming languages consume more energy when compilation flags are minimized. However, in some cases we could not prove that there was a difference in the energy consumption. We also find that the hardware used to run the programs has an influence on the energy consumption. We find that the runtime of a program correlates with the energy consumption, but a shorter runtime does not necessarily result in a lower energy consumption.

With the progress of low-power research on real-time systems, the estimation and analysis of energy consumption of real-time systems becomes a hot topic. Process Algebra is a formal method fit for analyzing the functional properties of real-time systems, but it can not analyze the energy consumption properties. This paper proposes a formal method support for modeling and analyzing energy consumption of real-time software. Resource Timed Communicating Sequential Process (RTCSP) is proposed in this paper can handle it efficiently by extending resource information on Timed Communicating Sequential Process (TCSP). In this paper, the power consumption of instructions in real-time systems is mapped into the resource of RTCSP, the energy consumption of real-time software can be modeled and optimized by using RTCSP, the optimal path algorithm is proposed to calculate the minimum energy consumption reachability path of real-time systems. This formal method improves the accuracy and efficiency of energy calculation, the calculation results can be used to quantitatively analyze and optimize the energy consumption of real-time systems.

ABSTRACTInterest in the energy consumption of software has grown with rising energy costs and greater environmental awareness. Many approaches to research in this area have been proposed, from the examination of hardware and compiler optimizations to platform specific software modifications. However, the impact of general software design on energy efficiency remains unclear. The goal of this research is to summarize the findings of studies that empirically examine the impact of design patterns, code smells, and refactorings (which we collectively describe as design elements) on energy consumption. Our secondary goal is to provide an overview of the impact of these aspects of software design on energy performance and discuss the current state of the art. We present a systematic literature review (SLR) of papers that examine the impact of the aforementioned design elements on energy consumption. We perform a search through four major databases, a manual search through publications of eight conferences and five journals from 2010 through 2023, in addition to snowballing. We extract relevant data from the literature and present an overview of each experiment's setup, the data reported, and results for each design element studied. Beginning with a set of 8684 papers, we select 24 that include studies of these design elements. Overall, they provide data on 22 design patterns, 17 code smells, and 31 refactorings. Many studies are preliminary in nature, and contradictory findings are frequent. We present three main findings: (i) a wide array of design patterns, code smells, and refactorings have been examined from an energy performance perspective; (ii) many of these studies are preliminary in nature and indicate the need for further research; (iii) there has been little growth recently in publications empirically examining these aspects of software design.

Models of energy consumption and performance are necessary to understand and identify system behavior, prior to designing advanced controls that can balance out performance and energy use. This paper considers the energy consumption and performance of servers running a relatively simple file-compression workload. We found that standard techniques for system identification do not produce acceptable models of energy consumption and performance, due to the intricate interplay between the discrete nature of software and the continuous nature of energy and performance. This motivated us to perform a detailed empirical study of the energy consumption and performance of this system with varying compression algorithms and compression levels, file types, persistent storage media, CPU DVFS levels, and disk I/O schedulers. Our results identify and illustrate factors that complicate the system's energy consumption and performance, including nonlinearity, instability, and multi-dimensionality. Our results provide a basis for future work on modeling energy consumption and performance to support principled design of controllable energy-aware systems.

During recent years, microprocessor energy consumption has been surging and efforts to reduce power and energy have received a lot of attention. At the same time, virtual execution environments (VEEs), such as Java virtual machines, have grown in popularity. Hence, it is important to evaluate the impact of virtual execution environments on microprocessor energy consumption. This paper characterizes the energy and power impact of two important components of VEEs, Just-in-time(JIT) optimization and garbage collection. We find that by reducing instruction counts, JIT optimization significantly reduces energy consumption, while garbage collection incurs runtime overhead that consumes more energy. Importantly, both JIT optimization and garbage collection decrease the average power dissipated by a program. Detailed analysis reveals that both JIT optimizer and JIT optimized code dissipate less power than un-optimized code. On the other hand, being memory bound and with low ILP, the garbage collector dissipates less power than the application code, but rarely affects the average power of the latter.Adaptive microarchitectures are another recent trend for energy reduction where microarchitectural resources can be dynamically tuned to match program runtime requirements. This research reveals that both JIT optimization and garbage collection alter a program's behavior and runtime requirements, which considerably affects the adaptation of configurable hardware units, and influences the overall energy consumption. This work also demonstrates that the adaptation preferences of the two VEE services differ substantially from those of the application code. Both VEE services prefer a simple core for high energy reduction. On the other hand, the JIT optimizer usually requires larger data caches, while the garbage collector rarely benefits from large data caches. The insights gained in this paper point to novel techniques that can further reduce microprocessor energy consumption.

The building construction materials are responsible for a large amount of energy and natural resource consumption. In light of the current challenges of resource scarcity and global climate change, the circular economy (CE) is a promising strategy to mitigate pressure on the environment, improve supplying of raw materials, and increase new market and employment opportunities. Developing eco-friendly thermal insulation materials based on agro-waste is a new waste management trend to achieve the sustainability of the resource and energy consumption in the construction sectors. In this work, banana-polystyrene composites were prepared by mixing the banana peels powder (BP) with polystyrene (PS) in different weight ratios (90:10, 80:20, 70:30, and 60:40). The physical and thermal properties such as thermal conductivity, electrical conductivity, Fourier Transform Infrared (FTIR), crystallographic structures of the fibers, X-Ray Diffraction (XRD), Thermogravimetric Analysis (TGA), and Differential Scanning Calorimetry (DSC) were carried out on BP and BP-PS1 that were prepared with ten wt.% and 20 wt.% of polystyrene powder (BP-PS2). The bio-composites results showed low thermal conductivity ranging from 0.028 to 0.030 W/m.K. The BP-PS2 exhibited a lower thermal conductivity of 0.027 W/m.K, while the pure peel powder demonstrated notable thermal stability, indicated by a total weight loss of 66.4% and a high crystallinity value of 56.1%. Furthermore, the thermal analysis (TGA) and X-Ray Diffraction (XRD) demonstrated that the pure banana peel has the highest thermal stability and crystallinity. These findings indicate that using banana peel-polystyrene composites represents an innovative solution for thermal insulation in buildings as an alternative to conventional materials to reduce energy and resource consumption.

In order to solve the problem of energy consumption prediction in the fused deposition process, an energy consumption prediction method is proposed by associating G-code files with fused deposition hardware. This method systematically studies the energy consumption in the process of fused deposition 3D printing by analyzing the relationship between G code files and fused deposition 3D printers. Starting from the energy consumption mechanism, a prediction model of fused deposition energy consumption for G code files is proposed. By obtaining the specific parameters of the fused deposition processing equipment and the selected experimental model, the experimental process parameter level is designed, and the orthogonal experiment is carried out to predict the energy consumption of the fusion processing in detail. The verification shows that the method can not only predict an accuracy rate of more than 90%, but also effectively predict the optimal process parameter value, which fully proves that the prediction method has high prediction accuracy and good practicability, and can be used for model processing. Provide tool support for energy consumption prediction before processing, saving production costs.

Previous research has established that trivial operations, i.e., instructions whose outcome can be trivially inferred from the operands, e.g. addition of zero, account for a quite significant portion of the dynamically executed instructions. By detecting them early and removing them from the pipeline, it is possible to reduce the energy consumption. This paper first presents a new classification of trivial operations in which especially such trivial operations that can be detected early, i.e. at the decode stage, in the pipeline are identified. Our analysis shows that on average as many as 10% of all executed instructions are of this kind across 12 applications from SPEC2000. We find that a majority (indeed 89%) of them are identity-trivial in which at least one of the operands is the identity element - zero or one. By detecting them early, one can bypass their execution and eliminate register accesses if the processor uses a logical/physical register remapping unit. We find that as many as 75% of all trivial operations can be detected and eliminated at the decode stage because the identity element is available that often. With such support, we find that the energy consumption in the functional units, the result bus, the instruction window infrastructure, and the register file can be reduced by 13%, 9%, 27%, and 26%, respectively yielding 18% reduction of the energy in the core pipeline