It sounds like you have independent tasks with more work to do than workers to do it. You also would like the tasks to be properly load-balanced. With these goals, parfor is the right tool. Note that parfor is a different construct than spmd in the Parallel Computing Toolbox.
Since you already have a cell array of file names and all the workers have access to the directory they are in, parfor can easily help you to load data from the files and use the data.
For example, if a different A variable is saved in each file, this code will allow 2 workers to process 5 files in an independent fashion without stomping on each other (no boolean array required).
matlabpool open local 2
inFiles = {'A.mat','B.mat', 'C.mat','D.mat','E.mat'};
outData = cell(1,length(inFiles));
parfor i=1:length(inFiles)
S = load(inFiles{i}, 'A');
outData{i} = S.A+3;
end
At the end of the computation, each worker has stored the result of its computation in a cell array with the same number of elements as the number of files processed.
Unfortunately, you cannot call save from within a parfor loop. You didn't mention how much data you need to save at each iteration. It may be that you can store the results as shown above until the parfor loop completes and then have a post-processing save step that executes outside of a parfor loop.
