Helping programmers improve the energy efficiency of source code

This paper briefly proposes a technique to detect energy inefficient fragments in the source code of a software system. Test cases are executed to obtain energy consumption measurements, and a statistical method, based on spectrum-based fault localization, is introduced to relate energy consumption to the system's source code. The result of our technique is an energy ranking of source code fragments pointing developers to possible energy leaks in their code.This work is financed by the ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Fundacao para a Ciencia e a Tecnologia within project POCI-01-0145-FEDER-016718 and UID/CEC/04516/2013; and by FLAD/NSF under the project Software Repositories for Green Computing, ref. 300/2015. The first author is also sponsored by FCT grant SFRH/BD/112733/2015

GreenC5: An Adaptive, Energy-Aware Collection for Green Software Development

Dynamic data structures in software applications have been shown to have a large impact on system performance. In this paper, we explore energy saving opportunities of interface-based dynamic data structures. Our results suggest that savings opportunities exist in the C5 Collection between 16.95% and 97.50%. We propose a prototype and architecture for creating adaptive green data structures by applying machine learning tools to build a model for predicting energy efficient data structures based on the dynamic workload. Our neural network model can classify energy efficient data structures based on features such as the number of elements, frequency of operations, interface and set/bag semantics. The 10-fold cross validation result show 95.80% average accuracy of these predictions. Our n-gram model can accurately predict the most energy efficient data structure sequence in 19 simulated and real-world programs - on average, with more than 50% accuracy and up to 98% using a bigram predictor. Our GreenC5 prototype demonstrates how a green data structure can be implemented. With a simple decision making technique, the data structure can efficiently adapt for energy efficiency with low overhead. The median of GreenC5\u27s potential energy savings is more than 60% and ranges from 18% to 95%

Batched Linear Algebra Problems on GPU Accelerators

The emergence of multicore and heterogeneous architectures requires many linear algebra algorithms to be redesigned to take advantage of the accelerators, such as GPUs. A particularly challenging class of problems, arising in numerous applications, involves the use of linear algebra operations on many small-sized matrices. The size of these matrices is usually the same, up to a few hundred. The number of them can be thousands, even millions. Compared to large matrix problems with more data parallel computation that are well suited on GPUs, the challenges of small matrix problems lie in the low computing intensity, the large sequential operation fractions, and the big PCI-E overhead. These challenges entail redesigning the algorithms instead of merely porting the current LAPACK algorithms. We consider two classes of problems. The first is linear systems with one-sided factorizations (LU, QR, and Cholesky) and their solver, forward and backward substitution. The second is a two-sided Householder bi-diagonalization. They are challenging to develop and are highly demanded in applications. Our main efforts focus on the same-sized problems. Variable-sized problems are also considered, though to a lesser extent. Our contributions can be summarized as follows. First, we formulated a batched linear algebra framework to solve many data-parallel, small-sized problems/tasks. Second, we redesigned a set of fundamental linear algebra algorithms for high- performance, batched execution on GPU accelerators. Third, we designed batched BLAS (Basic Linear Algebra Subprograms) and proposed innovative optimization techniques for high-performance computation. Fourth, we illustrated the batched methodology on real-world applications as in the case of scaling a CFD application up to 4096 nodes on the Titan supercomputer at Oak Ridge National Laboratory (ORNL). Finally, we demonstrated the power, energy and time efficiency of using accelerators as compared to CPUs. Our solutions achieved large speedups and high energy efficiency compared to related routines in CUBLAS on NVIDIA GPUs and MKL on Intel Sandy-Bridge multicore CPUs. The modern accelerators are all Single-Instruction Multiple-Thread (SIMT) architectures. Our solutions and methods are based on NVIDIA GPUs and can be extended to other accelerators, such as the Intel Xeon Phi and AMD GPUs based on OpenCL

Automatic load-balance method for coupled Earth System Models

Earth System Models (ESMs) are complex models used to simulate the Earth climate and are commonly built from different independent components that simulate a specific natural phenomenon (ocean dynamics, atmospheric dynamics, atmospheric chemistry, land and ocean biosphere, etc.). To simulate the interactions between these processes, ESMs use coupling libraries that manage the synchronization and field exchanges between the independent components, running in parallel in a typical Multi Program, Multiple Data (MPMD) application. The performance achieved depends on the coupling approach, and on the number of parallel resources and scalability properties of each component. Finding the best number of resources to use for each component of coupled ESMs is crucial to use the parallel resources efficiently. However, it is still a task involving manually testing multiple process allocations by trial and error, leading to configurations that are sub-optimal given that the dependencies between the constituents are complex and models do not scale perfectly. This project presents a methodology to find the optimal number of resources to allocate for each component to achieve the best computational performance for the coupled ESM, minimizing the cost of executing each of the constituents, which may not run at individual optimal configurations, and the waiting time due to the synchronizations between them. To achieve this, a number of novel metrics were designed and implemented in order to identify the component(s) acting as bottleneck(s) and to evaluate the performance of the coupled execution according to different Energy-To-Solution (ETS) / Time-To-Solution (TTS) tradeoff criteria. The methodology has been tested against multiple resource configurations used for the widely known ESM in Europe: EC-Earth3. The results show that some configurations could run up to 34% faster and reduce the execution cost by 6.7%. Moreover, the method has been contrasted against a configuration used for the Coupled Model Intercomparison Project Phase 6 (CMIP6)) and achieved a set-up 5% faster and 1% less costly. Lastly, the work has been integrated into a workflow manager to automatize the tasks, involving minimum user intervention

Energyware engineering: techniques and tools for green software development

Tese de Doutoramento em Informática (MAP-i)Energy consumption is nowadays one of the most important concerns worldwide. While hardware is generally seen as the main culprit for a computer’s energy usage, software too has a tremendous impact on the energy spent, as it can cancel the efficiency introduced by the hardware. Green Computing is not a newfield of study, but the focus has been, until recently, on hardware. While there has been advancements in Green Software techniques, there is still not enough support for software developers so they can make their code more energy-aware, with various studies arguing there is both a lack of knowledge and lack of tools for energy-aware development. This thesis intends to tackle these two problems and aims at further pushing forward research on Green Software. This software energy consumption issue is faced as a software engineering question. By using systematic, disciplined, and quantifiable approaches to the development, operation, and maintenance of software we defined several techniques, methodologies, and tools within this document. These focus on providing software developers more knowledge and tools to help with energy-aware software development, or Energyware Engineering. Insights are provided on the energy influence of several stages performed during a software’s development process. We look at the energy efficiency of various popular programming languages, understanding which are the most appropriate if a developer’s concern is energy consumption. A detailed study on the energy profiles of different Java data structures is also presented, alongwith a technique and tool, further providing more knowledge on what energy efficient alternatives a developer has to choose from. To help developers with the lack of tools, we defined and implemented a technique to detect energy inefficient fragments within the source code of a software system. This technique and tool has been shown to help developers improve the energy efficiency of their programs, and even outperforming a runtime profiler. Finally, answers are provided to common questions and misconceptions within this field of research, such as the relationship between time and energy, and howone can improve their software’s energy consumption. This thesis provides a great effort to help support both research and education on this topic, helps continue to grow green software out of its infancy, and contributes to solving the lack of knowledge and tools which exist for Energyware Engineering.Hoje em dia o consumo energético é uma das maiores preocupações a nível global. Apesar do hardware ser, de umaforma geral, o principal culpado para o consumo de energia num computador, o software tem também um impacto significativo na energia consumida, pois pode anular, em parte, a eficiência introduzida pelo hardware. Embora Green Computing não seja uma área de investigação nova, o foco tem sido, até recentemente, na componente de hardware. Embora as técnicas de Green Software tenham vindo a evoluir, não há ainda suporte suficiente para que os programadores possam produzir código com consciencialização energética. De facto existemvários estudos que defendem que existe tanto uma falta de conhecimento como uma escassez de ferramentas para o desenvolvimento energeticamente consciente. Esta tese pretende abordar estes dois problemas e tem como foco promover avanços em green software. O tópico do consumo de energia é abordado duma perspectiva de engenharia de software. Através do uso de abordagens sistemáticas, disciplinadas e quantificáveis no processo de desenvolvimento, operação e manutencão de software, foi possível a definição de novas metodologias e ferramentas, apresentadas neste documento. Estas ferramentas e metodologias têm como foco dotar de conhecimento e ferramentas os programadores de software, de modo a suportar um desenvolvimento energeticamente consciente, ou Energyware Engineering. Deste trabalho resulta a compreensão sobre a influência energética a ser usada durante as diferentes fases do processo de desenvolvimento de software. Observamos as linguagens de programação mais populares sobre um ponto de vista de eficiência energética, percebendo quais as mais apropriadas caso o programador tenha uma preocupação com o consumo energético. Apresentamos também um estudo detalhado sobre perfis energéticos de diferentes estruturas de dados em Java, acompanhado por técnicas e ferramentas, fornecendo conhecimento relativo a quais as alternativas energeticamente eficientes que os programadores dispõem. Por forma a ajudar os programadores, definimos e implementamos uma técnica para detetar fragmentos energicamente ineficientes dentro do código fonte de um sistema de software. Esta técnica e ferramenta têm demonstrado ajudar programadores a melhorarem a eficiência energética dos seus programas e em algum casos superando um runtime profiler. Por fim, são dadas respostas a questões e conceções erradamente formuladas dentro desta área de investigação, tais como o relacionamento entre tempo e energia e como é possível melhorar o consumo de energia do software. Foi empregue nesta tese um esforço árduo de suporte tanto na investigação como na educação relativo a este tópico, ajudando à maturação e crescimento de green computing, contribuindo para a resolução da lacuna de conhecimento e ferramentas para suporte a Energyware Engineering.This work is partially funded by FCT – Foundation for Science and Technology, the Portuguese Ministry of Science, Technology and Higher Education, through national funds, and co-financed by the European Social Fund (ESF) through the Operacional Programme for Human Capital (POCH), with scholarship reference SFRH/BD/112733/2015. Additionally, funding was also provided the ERDF – European Regional Development Fund – through the Operational Programmes for Competitiveness and Internationalisation COMPETE and COMPETE 2020, and by the Portuguese Government through FCT project Green Software Lab (ref. POCI-01-0145-FEDER-016718), by the project GreenSSCM - Green Software for Space Missions Control, a project financed by the Innovation Agency, SA, Northern Regional Operational Programme, Financial Incentive Grant Agreement under the Incentive Research and Development System, Project No. 38973, and by the Luso-American Foundation in collaboration with the National Science Foundation with grant FLAD/NSF ref. 300/2015 and ref. 275/2016

Energy efficiency across programming languages: how do energy, time, and memory relate?

This paper presents a study of the runtime, memory usage and energy consumption of twenty seven well-known software languages. We monitor the performance of such languages using ten different programming problems, expressed in each of the languages. Our results show interesting findings, such as, slower/faster languages consuming less/more energy, and how memory usage influences energy consumption. We show how to use our results to provide software engineers support to decide which language to use when energy efficiency is a concern.We would like to thank Luis Cruz (University of Porto) for the help that he provided. This work is financed by the ERDF - European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the Portuguese funding agency, FCT - Fundacao para a Ciencia e a Tecnologia within project POCI-01-0145-FEDER-016718 and UID/EEA/50014/2013. The first author is also sponsored by FCT grant SFRH/BD/112733/2015

Analyzing Performance, Energy Consumption, and Reliability of Mobile Applications

Mobile applications have become a high priority for software developers. Researchers and practitioners are working toward improving and optimizing the energy efficiency and performance of mobile applications due to the capacity limitation of mobile device processors and batteries. In addition, mobile applications have become popular among end-users, developers have introduced a wide range of features that increase the complexity of application code. To improve and enhance the maintainability, extensibility, and understandability of application code, refactoring techniques were introduced. However, implementing such techniques to mobile applications affects energy efficiency and performance. To evaluate and categorize software implementation and optimization efficiency, several metrics are introduced, such as the Greenup, Powerup, and Speedup (GPS-UP) metrics. The first contribution in my work is to quantitatively evaluate the impact of several refactoring techniques on the energy efficiency and performance of Fowler\u27s sample code in mobile environments. In addition, I introduce two new categories to the GPS-UP metrics to better categorize the impact of refactoring techniques on mobile applications. Moreover, I explain the interrelationship between energy efficiency and performance to provide more knowledge and insight for mobile application developers. Hence Fowler\u27s sample code is simple and does not reflect an accurate evaluation of the refactoring techniques, I extend my work through presenting a case study that evaluates and categorizes the impact of refactoring techniques when they are applied to open-source mobile applications. In addition, I provide a comparison of the effect of refactoring techniques between the results of Fowler\u27s sample and open-source mobile applications. The results of this contribution will allow software engineers and developers to understand the trade-offs between performance, energy efficiency, and maintainability when implementing refactoring techniques. The second contribution in my work is to modify the Orthogonal Defect Classification (ODC) model to accommodate defects of mobile applications. The ODC model enables developers to classify defects and track the process of inspection and testing. However, ODC was introduced to classify defects of traditional software. Mobile applications differ from traditional applications in many ways; they are susceptible to external factors, such as screen and network changes, notifications, and phone interruptions, which affect the applications\u27 functioning. The adapted ODC model allows me to address newly introduced application defects found in the mobile domain, such as energy, notification, and Graphical User Interface (GUI). In addition, based on the new model, I classify found defects of two well-known mobile applications. Moreover, I discuss one-way and two-way analyses. This contribution provides developers with a suitable defect analysis technique for mobile applications. Software reliability is an important quality attribute, and software reliability models are frequently used to measure and predict software maturity. The nature of mobile environments differs from that of PC and server environments due to many factors, such as the network, energy, battery, and compatibility. Evaluating and predicting mobile application reliability are real challenges because of the diversity of the mobile environments in which the applications are used, and the lack of publicly available defect data. In addition, bug reports are optionally submitted by end-users. In the third contribution of my dissertation, I propose assessing and predicting the reliability of a mobile application using known software reliability growth models (SRGMs). Four software reliability models are used to evaluate the reliability of an open-source mobile application through analyzing bug reports. The results of my work enable software developers and testers to assess and predict the reliability of mobile software applications