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This protocol leverages the capabilities of AiiDA, an open-source platform to manage and automate scientific computational workflows, and the TCOD, an open-access database storing computed materials properties using a well-defined and exhaustive ontology.
In terms of big data analytics, the challenges are mostly scientific, computational, and analytical in nature.
This goes against the vision of agile "pluggable", component-oriented, iterative workflow construction, central to scientific computational use cases.
For variations of analysis and calculations with atomic coordinates, the portability and extensibility illustrated by MOSBY play an important rule in scientific computational tools with active software development.
We implemented this method along with other two master-worker schemes (a previously existing one and a recently modified one) for three different scientific computational kernels.
This technique is designed to facilitate automatic parallelization of scientific computational applications that construct cyclic graphs to represent adjacent lists, which in turn describe the interactions of simulated objects.
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This approach could be included in courses related to computer organization, programming, discrete mathematics, numerical methods or scientific computing in computational science curricula.
The complexity and computational intensity of scientific computing has fueled research on parallel computing and performance analysis.
It also communicates with the scientific computation module to obtain the intermediate computational results.
GPUs, with their high bandwidths and computational capabilities are an increasingly popular target for scientific computing.
In addition to supporting scientific research, Computational Neuroscience can be used to create computational models for teaching neuroscience, and thus for teaching electrophysiology [ 1- 5].
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