Optimizing Data Storage

Grid Computing Data Considerations

Grid computing has become an important technology for organizations that:

have long-running applications that can benefit from parallel execution
want to leverage existing IT infrastructure to optimize computing resources and manage data and computing workloads

The function of a grid is to distribute tasks. Each of the tasks that are distributed across the grid must have access to all the required input data. Computing tasks that require substantial data movement generally do not perform well in a grid. To achieve the highest efficiency, the nodes should spend the majority of the time computing rather than communicating. With grid computing using SAS Grid Manager, the speed at which the grid operates is related more to the storage of the input data than to the size of the data.

Data must either be distributed to the nodes before running the application or-- much more commonly--made available through shared network libraries. Storage on local nodes is discouraged. The data storage must scale to maintain high performance while serving concurrent data requests.

The parallel data load is monitored throughout.

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