The inv function is multithreaded. You don't need to do anything to make use of multithreading here.
Here are more details:
When the documentation says that a function can be used in a thread-based environment, it means that you can run it on a thread-based worker. That is, you could do something like
parfor i=1:10
A = somefunctionToBuildA();
result{i} = inv(A)
end
and it would work on a ThreadPool. I.e. Parallel computing in MATLAB: Have you tried ThreadPools yet? » The MATLAB Blog - MATLAB & Simulink
It's about inverting many matrices in parallel rather than using multiple threads to invert one matrix.
The parallelism you are interested here is different. You want to think about built in Multithreading MATLAB Multicore - MATLAB & Simulink. Many functions are implicitly multithreaded and inv is probably one of them. There is not a definitive list of such functions but there are ways to find out.
By default, MATLAB will use a number of threads equal to the number of cores on your machine. You can check with the maxNumCompThreads function. On my machine:
>> maxNumCompThreads()
ans =
8
Let's do an experiment to see if inv is muiltthreaded.
>> a = rand(10000);
>> tic;b = inv(a);toc
Elapsed time is 10.543774 seconds.
I now tell MATLAB to use 1 thread and repeat the calculation
>> maxNumCompThreads(1);
>> tic;b = inv(a);toc
Elapsed time is 34.646851 seconds.
That's more than 3x slower than when I used 8 threads. So, I conclude that inv is making use of multithreading.
