Matlab`s parallel computing toolkits enables you batch and breakup large data set processing

We all know about parallel computing capabilities. C++ is quite powerful at doing it but you need to hand code everything to take advantage of it. It seems Matlab includes a parallel computing toolkit which makes life easier.

You can break up the complicated and long running processes into small worker group sets. This allows you to run Matlab more efficiently and your tasks will take a shorter amount of time to complete. Matlab enables you run as many remote workers as you can based on the license you get from Mathworks. Out of the box, it seems Matlab limits you to eight workers running in parallel.

Some uses for this parallel computing feature is when you have long iterations running or you have too many running. You could use the parfor function instead of the standard for loop to take full advantage of the parallel computing capabilities. You could also batch jobs or shorten long data sets using this Matlab parallel computing toolkit.

With the parallel computing toolkit, you can get:

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Different Array Types

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Working with Co distributed Arrays

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Using a for-Loop Over a Distributed Range (for-drange)

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Using MATLAB Functions on Codistributed Arrays

Thankfully, Matlab`s help files give you a run down on this very powerful feature which many application cannot offer. I am very, very impressed with this capability which includes the capability to run a profiler that simultaneously displays each worker. It also has the ability to talk to most scheduling software programs which makes Matlab`s parallel computing features even better. Wow! You need to check out some of the powerful function calls to be make this happen.

Also, this parallel computing toolkits makes Matlab a very modern architecture as you may want to consider using instead of pure C++ home build applications.

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