MATLAB Matrix Preallocation Slower Than Dynamic Matrix Expansion

In each iteration of a loop, I am calculating a MATLAB matrix. These matrices all must be concatenated together to create one final matrix. I know the dimensions of this final matrix before entering the loop, so I though preallocating the matrix using the 'zeros' function would be faster than initializing an empty array then simply appending the subarrays in each iteration of my loop. Oddly, my program runs MUCH slower when I preallocate. Here is the code (Only the first and last lines differ):

This is slow:

w_cuda = zeros(w_rows, w_cols, f_cols);

for j=0:num_groups-1

    % gets # of rows & cols in W. The last group is a special
    % case because it may have fewer than max_row_size rows
    if (j == num_groups-1 && mod(w_rows, max_row_size) ~= 开发者_Go百科0)
        num_rows_sub = w_rows - (max_row_size * j);    
    else
        num_rows_sub = max_row_size;
    end;

    % calculate correct W and f matrices
    start_index = (max_row_size * j) + 1;
    end_index = start_index + num_rows_sub - 1;

    w_sub = W(start_index:end_index,:);
    f_sub = filterBank(start_index:end_index,:);

    % Obtain sub-matrix
    w_cuda_sub = nopack_cu(w_sub,f_sub);

    % Incorporate sub-matrix into final matrix
    w_cuda(start_index:end_index,:,:) = w_cuda_sub;

end

This is fast:

w_cuda = [];

for j=0:num_groups-1

    % gets # of rows & cols in W. The last group is a special
    % case because it may have fewer than max_row_size rows
    if (j == num_groups-1 && mod(w_rows, max_row_size) ~= 0)
        num_rows_sub = w_rows - (max_row_size * j);    
    else
        num_rows_sub = max_row_size;
    end;

    % calculate correct W and f matrices
    start_index = (max_row_size * j) + 1;
    end_index = start_index + num_rows_sub - 1;

    w_sub = W(start_index:end_index,:);
    f_sub = filterBank(start_index:end_index,:);

    % Obtain sub-matrix
    w_cuda_sub = nopack_cu(w_sub,f_sub);

    % Incorporate sub-matrix into final matrix
    w_cuda = [w_cuda; w_cuda_sub];

end

As far as other potentially useful information--my matrix is 3D, and the numbers within it are complex. As always, any insight is appreciated.

I have always assumed preallocation is faster for any array size and never actually tested it. So, I did a simple test timing the population of various array sizes from 1x1x3 up to 20x20x3 using 1000 iterations by both appending and preallocation methods. Here's the code:

arraySize = 1:20;
numIteration = 1000;

timeAppend = zeros(length(arraySize), 1);
timePreAllocate = zeros(length(arraySize), 1);

for ii = 1:length(arraySize); 
    w = [];
    tic;
    for jj = 1:numIteration
        w = [w; rand(arraySize(ii), arraySize(ii), 3)];
    end
    timeAppend(ii) = toc;
end; 

for ii = 1:length(arraySize); 
    w = zeros(arraySize(ii) * numIteration, arraySize(ii), 3);
    tic;
    for jj = 1:numIteration
        indexStart = (jj - 1) * arraySize(ii) + 1;
        indexStop = indexStart + arraySize(ii) - 1;
        w(indexStart:indexStop,:,:) = rand(arraySize(ii), arraySize(ii), 3);
    end
    timePreAllocate(ii) = toc;
end; 

figure;
axes;
plot(timeAppend);
hold on;
plot(timePreAllocate, 'r');
legend('Append', 'Preallocate');

And here are the (as expected) results: