User`s guide

Key Problems Addressed by Parallel Computing
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the same machine as the client, you might see significant performance improvement on a
multicore/multiprocessor machine. So whether your loop takes a long time to run because
it has many iterations or because each iteration takes a long time, you can improve your
loop speed by distributing iterations to MATLAB workers.
Execute Batch Jobs in Parallel
When working interactively in a MATLAB session, you can offload work to a MATLAB
worker session to run as a batch job. The command to perform this job is asynchronous,
which means that your client MATLAB session is not blocked, and you can continue your
own interactive session while the MATLAB worker is busy evaluating your code. The
MATLAB worker can run either on the same machine as the client, or if using MATLAB
Distributed Computing Server, on a remote cluster machine.
Partition Large Data Sets
If you have an array that is too large for your computer's memory, it cannot be easily
handled in a single MATLAB session. Parallel Computing Toolbox software allows you
to distribute that array among multiple MATLAB workers, so that each worker contains
only a part of the array. Yet you can operate on the entire array as a single entity. Each
worker operates only on its part of the array, and workers automatically transfer data
between themselves when necessary, as, for example, in matrix multiplication. A large
number of matrix operations and functions have been enhanced to work directly with
these arrays without further modification; see “MATLAB Functions on Distributed and
Codistributed Arrays” on page 5-24 and “Using MATLAB Constructor Functions” on
page 5-9.