Question about Parfor-loop

I do nonlinear fitting within a nested for loop as shown below:

   for i=1:200
       for j=1:170
            for k=1:50
                %do nonlinear fitting
                [p,res] = lsqnonlin()
                params(i,j,k,1:5)=[p,fix_par(i,j,k),res];
            end
        end
    end

The above loop is extremely slow and I am trying to find ways to speed it up. I've read about the parlour-loop (which I am not familiar at all - I'm new to programming). I tried to use it but I got the following error "The variable params in a parfor cannot be classified." I tried something as below:

parfor i=1:200
       parfor j=1:170
            parfor k=1:50
                %do nonlinear fitting
                [p,res] = lsqnonlin()
                params(i,j,k,1:5)=[p,fix_par(i,j,k),res];
            end
        end
    end

[Adam]

This page should help.

You cannot use nested parfor loops though, you can only use it on one loop (usually the outer loop is best if it is possible to parallelise at this level). This is intuitive from the fact that if the outer loop is using all workers then the loop nested inside it clearly cannot spread the work to all workers within each worker as they are all already being used.

[Steven Lord]

Why do you need to perform 1.7 million individual fits? Perhaps there's a way to achieve your ultimate goal without calling lsqnonlin almost 2 million times. If you describe what you're trying to do we may be able to offer suggestions about a faster alternative approach.

[Mar]

My data are 4D images with dimensions [200 170 50 31]. I am doing pixel-wise fitting that's why I am calling lsqnonlin so many times. Is there any alternative way to suggest?