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Five big ideas

Computational problems

There are many different ways to describe computational problems. Here are a few that are important to grid technology:


Embarrassingly parallel calculations: A calculation is embarrassingly parallel when each sub-calculation is independent of all the other calculations. For example, analyzing a large databank of medical images is embarrassingly parallel, since each image is independent of the others.


Fine-grained calculations: In a fine-grained calculation, each sub-calculation is dependent on the result of another sub-calculation. For example, when calculating the weather, each calculation in one volume of atmosphere is affected by surrounding volumes. Fine-grained parallel calculations require very clever programming to make the most of their parallelism, so that the right information is available to processors at the right time.

High-performance vs. high-throughput:


And grid computing..?
Many interesting problems in science require a combination of fine- and coarse-grained calculations, and this is where grids can be particularly powerful:

For example, in the case of complex climate modeling, researchers launch many similar calculations to see how different parameters affect their models. Each calculation is a fine-grained parallel calculation that needs to run on a single cluster or supercomputer. Using a grid, these many independent calculations can be distributed over many different grid clusters, thus adding coarse-grained parallelism and saving a lot of time.