As mobile applications, the Internet of Things, and cyber-physical systems become more prevalent, so there is an increasing focus on energy efficiency of multicore computing applications. At the same time, traditional performance issues remain equally important. Increasingly, software designs need to find the best performance within some energy budget—often while also respecting real-time or other constraints, which may include security, data locality or system criticality— and while simultaneously optimising the usage of the available hardware resources. While parallel multi- core/manycore hardware can, in principle, ameliorate energy problems, and heterogeneous systems can help to find a good balance between performance and energy usage, at present there are no effective analyses beyond user-guided simulations that can reliably predict energy usage for parallel systems, whether alone or in combination with timing information. In order to create energy-efficient parallel software, programmers need to be actively engaged in decisions about energy usage, performance etc. rather than passively informed about their effects. This extends to design-time as well as to implementation-time and run-time. In order to address this fundamental challenge, TeamPlay takes a radically new approach: by exploiting new and emerging ideas that allow extra-functional properties to be deeply embedded within their programs, programmers can be empowered to directly treat energy usage, time, and other key system properties, as first-class citizens in their parallel software.
The TeamPlay project aimed to develop new, formally-motivated, techniques that allow execution time, energy usage, security (ETS), and other important non-functional properties (NFP) of parallel software to be treated effectively, and as first- class citizens. We build this into a toolbox to develop parallel software for low-energy systems, as required by the internet of things, cyber-physical systems etc. The TeamPlay approach allows programs to reflect directly on their own ETS, etc., as well as enables the developer to reason about both the functional and the NFP of their software at source code level.
Our success ensures significant progress on a pressing problem of major industrial importance: how to effectively manage energy consumption for parallel systems while maintaining the right balance with other important software metrics, including time, security, code size/complexity, etc. The project brings together leading industrial and academic experts in parallelism, energy modeling/transparency, worst-case execution time analysis, non-functional property analysis, compilation, security, and task coordination. Results are evaluated with industrial use cases taken from the computer vision, satellites, flying drones, medical domains.
