Data-Aware Support for Hybrid HPC and Big Data Applications.

Nowadays there is a raising interest in bridging the gap between Big Data application models and data-intensive HPC. This work explores the effects that Big Data-inspired paradigms could have in current scientific applications through the evaluation of a real-world application from the hydrology domain. This evaluation led to experience that portrayed the key aspects of the HPC and Big Data paradigms that made them successful in their respective worlds. With this information, we established a research roadmap to build a platform suitable for HPC hybrid applications, with a focus on efficient data management and fault-tolerance.