Making a for-loop in Matlab faster by using arrayfun?

currently I have the following portion of code:

for i = 2:N-1
  res(i) = k(i)/m(i)*x(i-1) -(c(i)+c(i+1))/m(i)*x(N+i) +e(i+1)/m(i)*x(i+1);
end

where as the variables k, m, c and e are vectors of size N and x is a vector of size 2*N. Is there any way to do this a lot faster using something like arrayfun!? I couldn't figure this out :( I especially want to make it faster by running on the GPU later and thus, array开发者_运维技巧fun would be also helpfull since matlab doesn't support parallelizing for-loops and I don't want to buy the jacket package... Thanks a lot!

You don't have to use arrayfun. It works if use use some smart indexing:

    clear all

    N=20;
    k=rand(N,1);
    m=rand(N,1);
    c=rand(N,1);
    e=rand(N,1);
    x=rand(2*N,1);

    % for-based implementation
    %Watch out, you are not filling the first element of forres!
    forres=zeros(N-1,1); %Initialize array first to gain some speed.
    for i = 2:N-1
      forres(i) = k(i)/m(i)*x(i-1) -(c(i)+c(i+1))/m(i)*x(N+i) +e(i+1)/m(i)*x(i+1);
    end

    %vectorized implementation
    parres=k(2:N-1)./m(2:N-1).*x(1:N-2) -(c(2:N-1)+c(3:N))./m(2:N-1).*x(N+2:2*N-1) +e(3:N)./m(2:N-1).*x(3:N);

    %compare results; strip the first element from forres
    difference=forres(2:end)-parres %#ok<NOPTS>

Firstly, MATLAB does support parallel for loops via PARFOR. However, that doesn't have much chance of speeding up this sort of computation since the amount of computation is small compared to the amount of data you're reading and writing.

To restructure things for GPUArray "arrayfun", you need to make all the array references in the loop body refer to the loop iterate, and have the loop run across the full range. You should be able to do this by offsetting some of the arrays, and padding with dummy values. For example, you could prepend all your arrays with NaN, and replace x(i-1) with a new variable x_1 = [x(2:N) NaN]