In the last decade, the scientific computing scenario is greatly evolving in two main areas. First, the focus on scientific computation is changing from CPU-intensive jobs, like large scale simulations or complex mathematical applications, towards a data-intensive approach. This new paradigm greatly affects the underlying architecture requirements, slowly vanishing the classical CPU bottleneck and exposing bottlenecks in the I/O systems. Second, the evolution in computing technologies and science funding restrictions are changing the available computing resources in the scientific community. Exascale computing faces many challenges, as the parallel codes will need to control millions of threads running on many cores. Such programs will need to avoid synchronization, use less communication and memory, and failures could be more frequent. Whilst these considerations are critically important to the scalability of future codes, the programmers themselves typically want to concentrate on their own application and not have to deal with these lower level, tricky, details. Today no available programming models and languages provide solutions to these issues, specially when data intensive applications are involved. Therefore, new programming models are required to handle these challenges.
The ASPIDE project aims to provide programming models to assist developers in building data-intensive applications for Exascale systems, while ensuring compliance with requested data management and performance.
• O1. Design and develop of a new Exascale programming models for extreme data applications.
• O2. Build new tools for monitoring extreme data analytics algorithms and applications.
• O3. Adapt the data management techniques to the extreme scale applications.
• O4. Validate the concepts and tools usefulness through extreme data applications.
The project’s main social indirect benefit will be generated by the development of a energy-aware support system for extreme data processing that will allow to reduce the energy consumed by the supercomputing centers (benefits to health by less pollution, global warming avoidance by less CO2, public cost reductions). The project is expected also to provide user-friendly APIs and tools for extreme data application development in the supercomputing field. This will provide an excellent opportunity for extending its usage to communities that are now constrained by complexity, thus allowing to solve new challenges and probably reducing unemployment in the application sectors.
The project activities include experimentation with human brain is the less known organ of the human body. Brain morphology is in continuous change across life span and its biological and functional implications remain unclear. After decades of developments in Magnetic Resonance Imaging (MRI), neuroimaging has become a key technique for the evaluation and quantification, in vivo, of brain maturation changes related to neuro development, aging, learning or disease. Moreover, the project activities includes a urban computing application development that will be aligned with the efforts of evolving towards smart cities and will provide a demonstrator of how urban data can be exploited for social good.
