In some cases we need to apply functions to a set of ND-arrays. Let's look at this simple example.

```
A(:,:,1) = [1 2; 4 5];
A(:,:,2) = [11 22; 44 55];
B(:,:,1) = [7 8; 1 2];
B(:,:,2) = [77 88; 11 22];
A =
ans(:,:,1) =
1 2
4 5
ans(:,:,2) =
11 22
44 55
>> B
B =
ans(:,:,1) =
7 8
1 2
ans(:,:,2) =
77 88
11 22
```

Both matrices are 3D, let's say we have to calculate the following:

```
result= zeros(2,2);
...
for k = 1:2
result(i,j) = result(i,j) + abs( A(i,j,k) - B(i,j,k) );
...
if k is very large, this for-loop can be a bottleneck since MATLAB order the data in a column major fashion. So a better way to compute "result" could be:
% trying to exploit the column major ordering
Aprime = reshape(permute(A,[3,1,2]), [2,4]);
Bprime = reshape(permute(B,[3,1,2]), [2,4]);
>> Aprime
Aprime =
1 4 2 5
11 44 22 55
>> Bprime
Bprime =
7 1 8 2
77 11 88 22
```

Now we replace the above loop for as following:

```
result= zeros(2,2);
....
temp = abs(Aprime - Bprime);
for k = 1:2
result(i,j) = result(i,j) + temp(k, i+2*(j-1));
...
```

We rearranged the data so we can exploit the cache memory. Permutation and reshape can be costly but when working with big ND-arrays the computational cost related to these operations is much lower than working with not arranged arrays.