One of the fundamental building blocks of a mobile application is the ability to persist program data between different invocations. Referred to as persistence, this functionality is commonly implemented by means of persistence frameworks. When choosing a particular framework, Android-the most popular mobile platform-offers a wide variety of options to developers. Unfortunately, the energy, performance, and programming effort trade-offs of these frameworks are poorly understood, leaving the Android developer in the dark trying to select the most appropriate option for their applications. To address this problem, this thesis reports on the results of the first systematic study of six Android persistence frameworks (i.e., ActiveAndroid, greenDAO, OrmLite, Sugar ORM, Android SQLite, and Realm Java) in their application to and performance with popular benchmarks, such as DaCapo. Having measured and analyzed the energy, performance, and programming effort trade-offs for each framework, we present a set of practical guidelines for the developer to choose between Android persistence frameworks. Our findings can also help the framework developers to optimize their products to meet the desired design objectives. / Master of Science

GPU-based heterogeneous architectures have been given much attention recently. How to get optimal performance out of those architectures with affordable programming effort remains a complex challenge. The PEPPHER framework is one possible solution. Within the PEPPHER framework, the StarPU run-time system is used to decrease such programming efforts, and at the same time to ensure near optimal performance by efficient scheduling over such architectures. However, adapting a normal C/C++ application to the StarPU runtime system requires additional programming effort. This thesis implements and tests a composition tool for automatic adaptation of normal C/C++ applications withPEPPHER components to StarPU. This composition tool requires XML annotation for applications and several trivial changes to applications, which take limited efforts. Our results obtained by three test cases (vector scale, sorting, andmatrix multiplication) show that automatic adaptation works well on different platforms that StarPU supports. It is also shown that besides StarPU’s dynamic composition, this tool facilitates static composition to improve performance portability of normal C/C++ applications.