- Environmental Science: To apply high performance computing to environmental applications such as forest fire, climate change, meteorology,...... The goal is to study new computational challenges and design appropriate solutions for this kind of multidisciplinary applications in the multi/many core era.
- Life Science:To apply high performance computing to life science applications such as bioinformatics, oncology, e-health.... The study of these applications is focused not only on reducing their execution time but also on the data management and efficiency.
- Programming models and execution environments: They must be reconsidered both regarding these new architectural trends (computation, communication and memory organization) and the requirements of the selected applications. An analysis of the suitability of existing solutions and the development of specific extensions is mandatory.
- Performance modelling, evaluation and tuning: Performance models are critical for efficient programming and tuning. They must be broaden or even redesigned to include the new features of current and future high-performance systems. Moreover, current monitoring and tuning mechanisms must be rethought to be effective in large-scale, multi/many core systems.
- GPU application development: Design, development and performance optimization of GPU/Multi-core/Computational accelerator science and engineering applications
- Resource Management Mechanisms:
Distribution, assignment, and scheduling policies will be analyzed in connection with the different types of system and application considered. New proposals will be investigated for cluster, cloud and peer-to-peer systems, which also care about core-level management.