Improving 3D medical image registration CUDA software with genetic programming

Genetic Improvement (GI) is shown to optimise, in some cases by more than 35%, a critical component of health-care industry software across a diverse range of six nVidia graphics processing units (GPUs). GP and other search based software engineering techniques can automatically op-timise the current rate limiting CUDA parallel function in the Nifty Reg open source C++ project used to align or reg-ister high resolution nuclear magnetic resonance NMRI and other diagnostic NIfTI images. Future Neurosurgery tech-niques will require hardware acceleration, such as GPGPU, to enable real time comparison of three dimensional in the-atre images with earlier patient images and reference data. With millimetre resolution brain scan measurements com-prising more than ten million voxels the modified kernel can process in excess of 3 billion active voxels per second

Optimising Darwinian Data Structures on Google Guava

Data structure selection and tuning is laborious but can vastly improve application performance and memory footprint. In this paper, we demonstrate how artemis, a multiobjective, cloud-based optimisation framework can automatically find optimal, tuned data structures and how it is used for optimising the Guava library. From the proposed solutions that artemis found, 27.45% of them improve all measures (execution time, CPU usage, and memory consumption). More specifically, artemis managed to improve the memory consumption of Guava by up 13%, execution time by up to 9%, and 4% CPU usage

Software is Not Fragile

Trying all simple changes (first order mutations) to executed C, C++ and CUDA source code shows software engineering artefacts are more robust than is often assumed. Of those that compile, up to 89 % run without error. Indeed a few mutants are improvements. Program fitness landscapes are smoother. Analysis of these programs, a parallel nVidia GPGPU kernel, all CUDA samples and the GNU C library shows many lines of code and integer values are repeated and may follow Zipf’s law

Darwinian Data Structure Selection

Data structure selection and tuning is laborious but can vastly improve an application's performance and memory footprint. Some data structures share a common interface and enjoy multiple implementations. We call them Darwinian Data Structures (DDS), since we can subject their implementations to survival of the fittest. We introduce ARTEMIS a multi-objective, cloud-based search-based optimisation framework that automatically finds optimal, tuned DDS modulo a test suite, then changes an application to use that DDS. ARTEMIS achieves substantial performance improvements for \emph{every} project in $5$ Java projects from DaCapo benchmark, $8$ popular projects and $30$ uniformly sampled projects from GitHub. For execution time, CPU usage, and memory consumption, ARTEMIS finds at least one solution that improves \emph{all} measures for $86\%$ ($37/43$) of the projects. The median improvement across the best solutions is $4.8\%$, $10.1\%$, $5.1\%$ for runtime, memory and CPU usage. These aggregate results understate ARTEMIS's potential impact. Some of the benchmarks it improves are libraries or utility functions. Two examples are gson, a ubiquitous Java serialization framework, and xalan, Apache's XML transformation tool. ARTEMIS improves gson by $16.5$\%, $1\%$ and $2.2\%$ for memory, runtime, and CPU; ARTEMIS improves xalan's memory consumption by $23.5$\%. \emph{Every} client of these projects will benefit from these performance improvements.Comment: 11 page

Genetic improvement: A key challenge for evolutionary computation

Automatic Programming has long been a sub-goal of Artificial Intelligence (AI). It is feasible in limited domains. Genetic Improvement (GI) has expanded these dramatically to more than 100 000 lines of code by building on human written applications. Further scaling may need key advances in both Search Based Software Engineering (SBSE) and Evolutionary Computation (EC) research, particularly on representations, genetic operations, fitness landscapes, fitness surrogates, multi objective search and co-evolution

Otimização para reduzir o tamanho de código-fonte JavaScript

JavaScript is one of the most used programming languages for front-end development of Web application. The increase in complexity of front-end features brings concerns about performance, especially the load and execution time of JavaScript code. To reduce the size of JavaScript programs and, therefore, the time required to load and execute these programs in the front-end of Web applications. To characterize the variants of JavaScript programs and use this information to build a search procedure that scans such variants for smaller implementations that pass all test cases. We applied this procedure to 19 JavaScript programs varying from 92 to 15,602 LOC and observed reductions from 0.2% to 73.8% of the original code, as well as a relationship between the quality of a program’s test suite and the ability to reduce its size.Esta Tese aborda o problema de otimização de tempo de carga de software, especificamente software escrito na linguagem de programação JavaScript, uma linguagem interpretada, baseada em objetos e amplamente utilizada no desenvolvimento de aplicativos e sistemas para a internet. Estudos experimentais foram projetados para avaliar a hipótese de que técnicas heurísticas já aplicadas com sucesso em linguagens orientadas a objeto poderiam ter resultados positivos na redução do tempo de carga de programas escritos em JavaScript. Para tanto, um ferramental que permitisse observar a aplicação de heurísticas selecionadas em programas JavaScript foi construído e executado em um ambiente de computação de alto desempenho. Os resultados dos estudos preliminares foram utilizados para criar um procedimento de busca que varre o código JavaScript criando variantes do programa que sejam menores e passem em todos os casos de teste do programa original. Aplicamos este procedimento a 19 programas JavaScript, variando de 92 a 15.602 linhas de código, e observamos reduções de 0,2% a 73,8% do código original, bem como uma relação entre a qualidade do conjunto de casos de testes e a capacidade de reduzir o tamanho dos programas