Optimising quantisation noise in energy measurement

We give a model of parallel distributed genetic improvement. With modern low cost power monitors; high speed Ethernet LAN latency and network jitter have little effect. The model calculates a minimum usable mutation effect based on the analogue to digital converter (ADC)’s resolution and shows the optimal test duration is inversely proportional to smallest impact we wish to detect. Using the example of a 1 kHz 12 bit 0.4095 Amp ADC optimising software energy consumption we find: it will be difficult to detect mutations which an average effect less than 58 μA, and typically experiments should last well under a second

Enhancing Genetic Improvement of Software with Regression Test Selection

Genetic improvement uses artificial intelligence to automatically improve software with respect to non-functional properties (AI for SE). In this paper, we propose the use of existing software engineering best practice to enhance Genetic Improvement (SE for AI). We conjecture that existing Regression Test Selection (RTS) techniques (which have been proven to be efficient and effective) can and should be used as a core component of the GI search process for maximising its effectiveness. To assess our idea, we have carried out a thorough empirical study assessing the use of both dynamic and static RTS techniques with GI to improve seven real-world software programs. The results of our empirical evaluation show that incorporation of RTS within GI significantly speeds up the whole GI process, making it up to 78% faster on our benchmark set, being still able to produce valid software improvements. Our findings are significant in that they can save hours to days of computational time, and can facilitate the uptake of GI in an industrial setting, by significantly reducing the time for the developer to receive feedback from such an automated technique. Therefore, we recommend the use of RTS in future test-based automated software improvement work. Finally, we hope this successful application of SE for AI will encourage other researchers to investigate further applications in this area

Exploring the Accuracy - Energy Trade-off in Machine Learning

Machine learning accounts for considerable global electricity demand and resulting environmental impact, as training a large deep-learning model produces 284 000kgs of the greenhouse gas carbon dioxide. In recent years, search-based approaches have begun to explore improving software to consume less energy. Machine learning is a particularly strong candidate for this because it is possible to trade off functionality (accu-racy) against energy consumption, whereas with many programs functionality is simply a pass-or-fail constraint. We use a grid search to explore hyperparameter configurations for a multilayer perceptron on five classification data sets, considering trade-offs of classification accuracy against training or inference energy. On one data set, we show that 77% of energy consumption for inference can saved by reducing accuracy from 94.3% to 93.2%. Energy for training can also be reduced by 30-50% with minimal loss of accuracy. We also find that structural parameters like hidden layer size is a major driver of the energy-accuracy trade-off, but there is some evidence that non-structural hyperparameters influence the trade-off too. We also show that a search-based approach has the potential to identify these trade-offs more efficiently than the grid search

Genetic improvement of GPU software

We survey genetic improvement (GI) of general purpose computing on graphics cards. We summarise several experiments which demonstrate four themes. Experiments with the gzip program show that genetic programming can automatically port sequential C code to parallel code. Experiments with the StereoCamera program show that GI can upgrade legacy parallel code for new hardware and software. Experiments with NiftyReg and BarraCUDA show that GI can make substantial improvements to current parallel CUDA applications. Finally, experiments with the pknotsRG program show that with semi-automated approaches, enormous speed ups can sometimes be had by growing and grafting new code with genetic programming in combination with human input

Specialising Software for Different Downstream Applications Using Genetic Improvement and Code Transplantation

OAPA Genetic improvement uses computational search to improve existing software while retaining its partial functionality. Genetic improvement has previously been concerned with improving a system with respect to all possible usage scenarios. In this paper, we show how genetic improvement can also be used to achieve specialisation to a specific set of usage scenarios. We use genetic improvement to evolve faster versions of a C++ program, a Boolean satisfiability solver called MiniSAT, specialising it for three applications. Our specialised solvers achieve between 4% and 36% execution time improvement, which is commensurate with efficiency gains achievable using human expert optimisation for the general solver. We also use genetic improvement to evolve faster versions of an image processing tool called ImageMagick, utilising code from GraphicsMagick, another image processing tool which was forked from it. We specialise the format conversion functionality to black & amp; white images and colour images only. Our specialised versions achieve up to 3% execution time improvement

Genetic Improvement of Software: a Comprehensive Survey

Genetic improvement (GI) uses automated search to find improved versions of existing software. We present a comprehensive survey of this nascent field of research with a focus on the core papers in the area published between 1995 and 2015. We identified core publications including empirical studies, 96% of which use evolutionary algorithms (genetic programming in particular). Although we can trace the foundations of GI back to the origins of computer science itself, our analysis reveals a significant upsurge in activity since 2012. GI has resulted in dramatic performance improvements for a diverse set of properties such as execution time, energy and memory consumption, as well as results for fixing and extending existing system functionality. Moreover, we present examples of research work that lies on the boundary between GI and other areas, such as program transformation, approximate computing, and software repair, with the intention of encouraging further exchange of ideas between researchers in these fields

Genetic Improvement of Software (Dagstuhl Seminar 18052)

We document the program and the immediate outcomes of Dagstuhl Seminar 18052 “Genetic Improvement of Software”. The seminar brought together researchers in Genetic Improvement (GI) and related areas of software engineering to investigate what is achievable with current technology and the current impediments to progress and how GI can affect the software development process. Several talks covered the state-of-the-art and work in progress. Seven emergent topics have been identified ranging from the nature of the GI search space through benchmarking and practical applications. The seminar has already resulted in multiple research paper publications. Four by participants of the seminar will be presented at the GI workshop co-located with the top conference in software engineering - ICSE. Several researchers started new collaborations, results of which we hope to see in the near future