The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
This work integrates two distinct. research areas of parallel and distributed computing, (1) automatic loop parallelization, and (2) component-based Grid programming. The latter includes technologies developed within CoreGRID for simplifying Grid programming: the Grid Component Model (GCM) and HigherOrder Components (HOCs). Components support developing applications on the P Grid without taking all the technical details of the particular platform type into account (network communication, heterogeneity, etc.). The GCM enables a hierarchical composition of program pieces and HOCs enable the reuse of component code in the development of new applications by specifying application-specific operations in a program via code parameters. When a programmer is provided, e.g., with a compute farm HOC., only the independent worker tasks must be described. But, once an application exhibits data or control dependences, the trivial farm is no longer sufficient. Here, the power of loop parallelization tools, like LooPo, comes into play: by embedding LooPo into a HOC, we show that these two technologies in combination facilitate the automatic transformation of a sequential loop nest with complex dependences (supplied by the user as a HOC parameter) into an ordered task graph, which can be processed on the Grid in parallel. This technique can significantly simplify GCM-based systems which combine multiple HOCs and other components. We use an equation system solver based on the successive overrelaxation method (SOR) as our motivating application example and for performance experiments.
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