By David Warne, a former QCIF eResearch Analyst at QUT.
MATLAB is a powerful programming environment for scientific applications. It is designed to be highly optimised for matrix and vector mathematical operations. Writing code in a way that leverages these matrix operations is known as vectorising code. There are many reasons why a particular piece of MATLAB code is running slow, but low usage of vectorised code is one of the most common ones that I see.
The differences between vectorised and non-vectorised code can be substantial. Consider a somewhat contrived example of matrix multiplication; a direct MATLAB implementation would be:
function [C] = slowMatMul(A,B)
[N,M] = size(A); [Q,P] = size(B);
if M ~= Q
error(‘Matrix dimension mismatch!’);
C = zeros(N,P);
C(i,j) = C(i,j) + A(i,k)*B(k,j);
Now, compare the runtime of the above code to MATLAB’s in-built matrix multiply for two 1000 x 1000 matrices (using a two-year-old Core i7 laptop):
>> tic; C = slowMatMul(A,B);toc;
Elapsed time is 9.948649 seconds.
>> tic; C = A*B;toc;
Elapsed time is 0.039372 seconds.
The in-built matrix mathematics is around 250x faster than direct MATLAB! There are lots of reasons for this, but for now, let’s just leave it at that.
Admittedly, this example is a bit artificial, but the point is to emphasise the potential computational gain that can be achieved through ensuring your code is as vectorised as possible. This may require a little more coding effort, but the results are well worth it.
For more details and examples, check out the following MATLAB documentation page.
This article was first published on 09/11/2017.