Thus, to ensure efficient execution of the workflow, Grid workflow scheduling is required to manage the allocation of Grid resources.
In order to achieve this, the execution environment of the Grid infrastructure establishes and exploits the synergies between the various modules of the architecture that participate in the management of the execution and the enforcement of the SLA contractual terms.
Unlike the conventional grid schedulers, CRB addresses several cases of failure of application scheduling such as nonavailability of enough computing nodes in a cluster, and, nonavailability of software execution environment in any of the grid resources.
In this paper, we present GRIDIFF, a software architecture that covers all necessary steps to integrate QoS into the process of allocating resources for the execution of jobs in the Grid.
We investigate time and energy to solution for the CPU- and GPU-based execution of the compute intensive smoother and grid transfer operators in a geometric multigrid linear solver.
In recent years there has been a significant effort to develop middleware that facilitates the execution of applications on Grid infrastructures.
Input parameters are defined as the dimensions of the grid and blocks assigned for execution on the GPU.
The complete availability of such information fosters information sharing and enables advanced application execution models and tools to be developed at the level of the grid.
Furthermore, by interfacing with grid managers (e.g. Oracle Grid Engine [49]) it is possible to distribute the execution of the analysis on the available (remote) resources on a computer grid and to maximize the use of the available licenses, while reducing the execution time (wall clock time) of the analysis task (see Fig. 5 ).
However, to achieve higher accuracy near curved boundaries in a voxelated domain, one has to increase the global density of the grid resulting in a significant increase in memory costs and execution time.
