• FPGA
• GPU
• machine learning
• device-aware scheduling
• algorithmic feature extraction
• heterogeneous execution
• high-level compiler
• Parallel programming
• functional programming
• abstract interpretation
• OpenCL
• Xeon Phi

The last few years has seen activity towards programming models, languages and frameworks to address the increasingly wide range and broad availability of heterogeneous computing resources through raised programming abstraction and portability across different platforms.

The effort spent in simplifying parallel programming across heterogeneous platforms is often outweighed by the need for low-level control over computation setup and execution and by performance opportunities that are missed due to the overhead introduced by the additional abstraction. Moreover, despite the ability to port parallel code across devices, each device is generally characterised by a restricted set of computations that it can execute outperforming the other devices in the system. The problem is therefore to schedule computations on increasingly popular multi-device heterogeneous platforms, helping to choose the best device among the available ones each time a computation has to execute.

Our Ph.D. research investigates the possibilities to address the problem of programming and execution abstraction on heterogeneous platforms while helping to dynamically and transparently exploit the computing power of such platforms in a device-aware fashion.