advantge of parallel system

One capability of MatLab is that its Parallel Computing Toolbox can automatically parcel out work based on the availability of units of computation, whether they are cores, clusters, or grids. Further, the Parallel Computing Toolbox enables users to run MatLab applications in up to eight cores locally, taking advantage of the latest trends in desktop system processors.
For those of you who write your own MatLab code, you can add some code to help it still more. Using parallel commands as a part of, for example, FOR loops (assuming that the repeating computations are independent), you can explicitly tell those loops to execute simultaneously on whatever resources are available. Rather than FOR, the MatLab instruction is PARFOR. And any code will execute on all defined resources in the project.
There is more to analysis than code. You may also have extremely large datasets. By breaking up problems on different computers in a cluster or grid, you can keep a smaller set of data in memory during parallel computation and still collect the data and analyze it later. If you can t run on a desktop system because of heap space limitations, maybe now you can.
The end result is you can take existing MatLab code and run it in parallel on multiple cores, often with few or no changes. Depending on where you are executing this code, you can get some pretty significant performance improvements. And best of all, you can largely use your existing MatLab routines.
While not everyone uses MatLab, it provides engineers with the opportunity to make full use of the power of their desktop computers and other inexpensive clusters and grids, including the Amazon EC2 cloud. Just be prepared to say goodbye to renting time on your favorite supercomputer.