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copied!<p>EDIT: MathWorks <a href="http://www.mathworks.com/support/tech-notes/1100/1107.html" rel="nofollow noreferrer">give advice on this problem</a>.</p> <hr> <p>You can view memory usage with the commands <code>system_dependent memstats</code> and <code>system_dependent dumpmem</code> (as well as simply <code>memory</code>, as noted by Jonas).</p> <p>The command <code>pack</code> (which in effect defragments your workspace) may also come in useful.</p> <p>If you are dealing with objects containing > 10 million or so values, then memory can easily become an issue. Throwing hardware at the problem (i.e. buying more RAM) may be an option, but there is a limit to what you can achieve.</p> <p>The way I suggest you approach recoding things to make them more memory efficient is: </p> <p>See if there are any variables you don't need to allocate. A classic example of this is when a function returns a value of the same size as it's input.</p> <pre><code>function x = XPlus1(x) x = x + 1; end </code></pre> <p>is more memory efficient than</p> <pre><code>function y = XPlus1(x) y = x + 1; end </code></pre> <p>Next, try and split your problem down into small chunks. At the simplest level, this may involve performing an operation on rows instead of a whole matrix, or on individual elements instead of a vector. (The cost of looping is less than the cost of it not running at all due to memory contraints.) Then you have to reconstruct your answer from the pieces.</p> <p>This step is essentially the philosophy behing map-reduce, so as a bonus, your code will be more easily parallelizable.</p>

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