MATLAB Programming

Using Memory Efficiently
• Allocate your larger matrices first, as explained in “Allocating Large
Matrices Earlier” on page 11-19.
• Set variables equal to the empty matrix [] to free memory, or clear the
variables using the clear function.
• Reuse variables as much as possible, but keeping in mind the guidelines
stated in “Assigning to Variables” on page 11-9.
Preallocating Arrays to Reduce Fragmentation
In the course of a MATLAB session, memory can become fragmented due
to dynamic memory allocation and deallocation. for and while loops that
incrementally increase, or grow, the size of a data structure each time through
the loop can add to this fragmentation as they have to repeatedly find and
allocate larger blocks of memory to store the data.
To make more efficient use of your memory, preallocate a block of memory
large enough to hold the matrix at its final size before entering the loop.
When you preallocate memory for a potentially large array, MATLAB “grabs”
sufficient contiguous space for the data at the beginning of the computation.
Once you have this space, you can add elements to the array without having
to continually allocate new space for it in memory.
For more information on preallocation, see “Preallocating Arrays” on page
11-7.
Allocating Large Matrices Earlier
MATLAB uses a heap method of memory management. It requests memory
from the operating system when there is not enough memory available in the
MATLAB heap to store the current variables. It reuses memory as long as the
size of the memory segment required is available in the MATLAB heap.
For example, on one machine these statements use approximately 15.4 MB
of RAM:
a = rand(1e6,1);
b = rand(1e6,1);
This statement uses approximately 16.4 MB of RAM:
11-19