Experiments with SmartGridSolve: Achieving Higher Performance by Improving the GridRPC Model

The paper presents SmartGridSolve, an extension of GridSolve, the programming system for high performance computing. The extension is aimed at higher performance of Grid applications by providing the functionality for collective mapping of a group of tasks on to a network topology that is fully connected. This functionality was achieved with only a minor addition to the GridRPC API. The key to the implementation of collective mapping was to separate the mapping of tasks from their execution which is one atomic operation in the GridRPC model of GridSolve.

    This paper demonstrates the performance gained by collective mapping with a real-life astrophysical experiment. The presented results show a significant speedup of 2.17 executing this application on a small network of two servers.