#Permute matlab code

Note, the tic() and toc() functions allow us to time parts of our code execution. To see what kind of difference array order makes, we can write the following test code. In the first orientation, the images will be arranged contiguously in memory, while in the second orientation it will not. There are two likely ways in which these images might be arranged in memory: or.

The images are of size ( nX, nY) pixels and we have nI images in our data set. Suppose we have a collection of images that we want to operate on. Let’s demonstrate this with a simple example. So having the knowledge of how our data is organized in memory can also help us write more computationally efficient code. It is much more efficient to access data that is contiguous in memory. So why does array order matter? Well, when you are looping over data, array order can actually have a significant impact on the run time.

The linear index at the bottom shows how the individual elements are actually stored in memory. The animation shows how a matrix is unwrapped using both conventions. To make this a little more clear, we can build a simple animation. Since MATLAB is column-major, you can see that the colMaj matrix returns the index in proper order, but the rowMaj matrix returns the elements out of order. We can trivially see how MATLAB stores the data by using the ‘:’ operator, which will unwrap the data. colMaj = disp( colMaj ) 1 4 7 2 5 8 3 6 9 rowMaj = 1 2 3 4 5 6 7 8 9 disp( rowMaj ) 1 is the first memory address, 2 is the second memory address, etc…). Similarly, you can think of the numbers in the matrix as the order of the data in memory (e.g. The following code snippet shows an example of a 3 x 3 matrix where each element shows what the linear index is. So basically, we count elements down the columns for column-major and along the rows for row-major. and in row-major order, the elements of rows are contiguous in memory. In column-major order, the elements of columns are contiguous in memory. These two different methods simply refer to which elements of the ND array area contiguous in memory, as well as, how such arrays should be linearly indexed. There are two general conventions: row-major and column-major ordering. When dealing with multi-dimensional or N-dimensional (ND) arrays it is important to be aware of how the data are stored in memory. In this tutorial, I introduce and illustrate the concept of array order, create a simple experiment to demonstrate the effects of array order when looping, and explain how the permute() command in MATLAB can be used to write efficient loops every time.Īll of the source code for the examples can be found in the GitHub repository. Choosing which dimension to loop over can actually have a significant impact on computational efficiency. Array order is an important consideration when looping over data in MATLAB/Octave.

Permute matlab