Which type of function call provides better performance in MATLAB?
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MathWorks Support Team on 23 Apr 2010
I have 7 different types of function call:
1. An inlined function, where the code author replaces the function call with a copy of the body of the function.
2. A function is defined in a separate MATLAB file. The arguments are passed by the calling function (file-pass).
3. A function is defined in a separate MATLAB file. The arguments are provided by referencing global variables; only indices are provided by the calling function (file-global).
4. A nested function. The arguments are passed by the enclosing function (nest-pass).
5. A nested function. The arguments are those shared with the enclosing function; only indices are provided by the enclosing function (nest-share).
6. A sub function. The arguments are passed by the calling function (sub-pass).
7. A sub function. The arguments are provided by referencing global variables; only indices are provided by the calling function (sub-global).
(For more information, please see the following three MATLAB files: testTop.m, testCompute, and testComputeGlobal.m)
I would like to know which function call provides better performance than the others in general.
MathWorks Support Team on 5 Oct 2018
Edited: MathWorks Support Team on 5 Oct 2018
The ordering of performance of each function call from the fastest to the slowest tends to be as follows:
inlined > file-pass = nest-pass = sub-pass > nest-share > sub-global > file-global
(A>B means A is faster than B and A=B means A is as fast as B)
First, using an inlined function is the fastest as it does not incur overhead associated with function call.
Second, when the arguments are passed to the callee function, the calling function sets up the arguments in such a way that the callee function knows where to retrieve them. This setup associated with function call in general incurs performance overhead, and therefore file-pass, nest-pass, and sub-pass are slower than inline.
Third, if the workspace is shared with nested functions and the arguments to a nested function are those shared within the workspace, rather than pass-by-value, then performance of that function call is inhibited. If MATLAB sees a shared variable within the shared workspace, it searches the workspace for the variable. On the other hand, if the arguments are passed by the calling function, then MATLAB does not have to search for them. The time taken for this search explains that type nest-share is slower than file-pass, nest-pass, and sub-pass.
Finally, when a function call involves global variables, performance is even more inhibited. This is because to look for global variables, MATLAB has to expand its search space to the outside of the current workspace. Furthermore, the reason a function call involving global variables appears a lot slower than the others is that MATLAB Accelerator does not optimize such a function call. When MATLAB Accelerator is turned off with the following command,
feature accel off
the difference in performance between inline and file-global becomes less significant.
Please note that the behaviors depend largely on various factors such as operating systems, CPU architectures, MATLAB Interpreter, and what the MATLAB code is doing.
More Answers (6)
Robert on 3 Jul 2018
Helpful stuff, but shouldn't the first alternative read "1. An Inline function. The body of the function is directly written down (inline)."?
Dominique on 13 Jun 2020
Edited: Walter Roberson on 7 Aug 2021
I was reading your comments on using global variables, which are expected to always slow down the running time of any code.
Please check the following code, run on R2019b.
There, you can see that, using a global variable speeds the code by more than a factor of hundred.
Using global variable
Elapsed time is 0.001173 seconds.
Elapsed time is 0.880516 seconds.
Please explain me what's going on here !
Thanks for your help,
% Using a global variable
disp('Using global variable')
% Doing the same thing passing variable to function
%Called function when using global variable
%Called function when passing variable
Cristian Le on 26 Jan 2021
With inline currently marked as not recomended, does the annonymous function have the same performance as inline right now?
Let's say I have two calls like in this example:
a = 1;
b = 2;
f_an = @(x) f(x,a,b);
% Substitute the variables as needed
f_in = inline(sprintf('f(x,%d,%d)',a,b),'x');
% Which is better when used as a wrapper function, e.g.
y = ode45(@f_an,..);
y = ode45(@f_in,..);
function y = f(x,a,b)
% Main function called multiple times
Are these equivalent in this example, or is the best implementation still calling a nested function?
Do the same arguments still hold when we are using C++ interface functions (custom generated with clibgen)?
broken_arrow on 20 Mar 2021
That leaves me a bit confused. Isn't an inlined function the same as just pasting the function code into a script (which would mean a script should be the most performant)? This post on the other hand suggests that functions are generally faster than scripts: https://de.mathworks.com/matlabcentral/answers/415728-details-on-why-functions-are-faster-than-scripts But if I pass variables to a function, the program would still have to look up the variables in the base workspace and launch the function, which creates overhead compared to a script. My "working hypothesis" used to be that everything that is executed only once is put in a script and functions are for code that is used often...