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6

Which type of function call provides better performance in MATLAB?

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.

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R2009a

3 Answers

Answer by MathWorks Support Team on 5 Oct 2018
Edited by MathWorks Support Team on 5 Oct 2018
 Accepted Answer

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.

  1 Comment

Also note that anonymous function calls are slower than direct function calls, but the difference can vary from almost negligible to being rather substantial in nearly identical code.
(I have some test cases that I put together a couple of years ago, but my system is loaded at the moment so I cannot do timing tests right now.)

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Answer by 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)."?

  1 Comment

Thank you for pointing out the error, we've updated the question accordingly.

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Answer by Hannes Greim on 31 Mar 2015

Do the answers change once the code is compiled and run with the MCR?

  1 Comment

The answer still remains the same once the code is compiled and run with MCR. This is because MATLAB Compiler works by running the same MATLAB code (although encrypted) on an installed runtime, and does not change the nature of the code. As such, the compiled MATLAB code will run with similar speed as if running on MATLAB desktop.

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