HLPGPU aims to bring together researchers and practitioners to present new results and ongoing work on those aspects of high-level programming relevant, or specific, to GPGPUs and new architectures.

Main Topics:

- High-level parallel programming models, libraries and languages.
- Adaptation to heterogeneous/hierarchical platforms: GPGPU, multicore, FPGA etc.
- Adaptation of cluster- and grid- performance models to new platforms.
- Programming experiments and applications of heterogeneous/hierarchical platforms with a view on high-level programming methods.
- Algorithmic skeletons, design patterns for heterogeneous/hierarchical platforms.
- Domain-specific parallel patterns
- Efficient implementations of parallel patterns on multicore systems
- Mapping patterns to heterogeneous multicore systems
- Exploiting performance information to guide pattern Implementation
- Pattern implementations using application-specific information
- Refactoring tools and techniques for parallel patterns
- Tools and systems to aid pattern-based parallel programming - structured
- Parallel programming models
- Heterogeneous computing
- Feedback-directed compilation
- Timing analysis tools and techniques
- Statistical performance modelling and prediction