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Today many DataGrid applications need to manage and process a very large amount of data distributed across multiple grid nodes and stored into heterogeneous databases.
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An advantage of the VESTAL approach is its high degree of sparseness in the functional activity images, with the majority (>85%) of grid nodes showing essentially zero (<0.001 nAm) activity, thereby reducing the problem of multiple comparisons in the spatial domain.
The reason for the project is a need on energy grids to constantly monitor the state of distant grid nodes.
The farther grid nodes have higher data transmission time than the nearer grid nodes.
The uniform, cubic computational grid had a size of 2403 grid nodes.
The best job distribution between grid nodes, and consequently the lowest grid idleness, occurred when the computational grid used Ourgrid.
Finite-difference discretization for an internal grid point involves then the surrounding grid nodes.
Generate a random path through the grid nodes.
Black crosses: X-Y distribution of grid nodes.
Fig. 9 Job distribution on grid nodes (Globus).
Simulations running on grid nodes receive the executing state.
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