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This paper attempts to show how the “big data” paradigm is changing science through offering access to millions of database elements in real time and the computational power to rapidly process those data in ways that are not initially obvious. In order to gain a proper understanding of these changes and their implications, we propose applying an extended cognition model to the novel scenario. [ABSTRACT FROM PUBLISHER]

Techniques for library-centric application design have already proven to be very useful in the past. The current gain in computer performance is shifted towards the utilisation of multi-core processors which extends the importance of this type of application design in the field of scientific computing, which also poses new difficulties. A parallel generic scientific simulation environment has been developed to ease this transition from single-core to multi-core systems without additional development activity. [ABSTRACT FROM AUTHOR]

The challenging art of multi-paradigmatic application development, which only few languages currently support, greatly aids the development of highly efficient and reusable software components. A link of two such languages, namely Python and C++, is presented. Thereby data structures and algorithms realised in C++ using features such as compile-time meta-programming are made available to the run-time environment of Python. Several generic components and modules for application design in the area of scientific computing are presented. Compile times and run-times are discussed to show the advantages of the proposed combination of both languages. [ABSTRACT FROM AUTHOR]