why my vectorized code perform weaker than unvectorized one?

i use "find" function to speed up the code, but surprisingly enough the vectorized code is slower than unvectorized one! i ran them in matlab 2013!

    function y = exam8(x)
    % Computes the sinc function per ? element for a set of x values.
    y = ones(size(x)); % Set y to all ones, sinc(0) = 1
    for k =1: length(x)
        if x(k)~=0
            y(k) = sin(x(k)) ./ x(k); 
        end
    end

    % vectorized code
    function y = exam9(x)
    % Computes the sinc function per ? element for a set of x values.
        y = ones(size(x)); % Set y to all ones, sinc(0) = 1
        i = find(x ~= 0); % Find nonzero x values
        y(i) = sin(x(i)) ./ x(i); % Compute sinc where x ˜= 0
    end

[per isakson]

Doesn't the code analyzer say that you should remove find in favor for logical indexing?

Why "surprisingly" ?

[David Young]

I also find the loop is fastest, even using logical indexing for the vectorised version (release 2013b under Windows). It's surprising because in the documentation we read that "Vectorized code often runs much faster than the corresponding code containing loops." If this doesn't apply to jabbar-kamali's example, it would be useful to have more guidance as to when it does apply.

[per isakson]

"it would be useful to have more guidance" &nbsp I definitely agree! &nbsp AFAIK: There are some fragmented guidance scattered all over the place, but no in-depth treatment of the topic. A quick search returned these two blog posts by Loren:

• Speeding Up MATLAB Applications
• Piecewise Linear Interpolation
• And there are hundreds of book to look in.

Answer 1

[the cyclist]

Here is another algorithm. (Notice that you don't need the preallocation step in this one.)

y = sin(x) ./ x;
y(isnan(y)) = 1;

The relative performance of the three algorithms is quite dependent on the proportion of zeros in your input.