Low performance with parallel SVM structure

Question

I have a problem for concurrency with SVM classification. In my code, I repeat 3 times the 5x SVM cross-validation and average its accuracy. I want to paralyze the main loop 3 times repeating cross-validation, but when I use parfor in this loop, the program does not use all the performance of the 2 cores of my processor and uses only 60% of the performance!

Thank you very much

Code:

parfor L=1:NumOFLoops;

indices = crossvalind('Kfold',QQ,NumOfKfold);
cp = classperf(QQ);

for m=1:NumOfKfold;

test = (indices == m); Train = ~test;
testI=zeros(numel(test>0),1);

TrainInputs=INPUT(Train,:);
TrainTargets=QQ(Train,:);
TestInputs=INPUT(test,:);

%% SVM Structure

  svmstruct=svmtrain(TrainInputs,TrainTargets,...
 'boxconstraint',Penalty,...
 'kernel_function','rbf','method','QP',...
 'rbf_sigma',Sigma)

  TestOutputs=svmclassify(svmstruct,TestInputs,'showplot',false);
  classperf(cp,TestOutputs,test);

end
Error(:,L)=cp.ErrorRate;         
end
Results.ErrorRate=(sum(Error))/NumOFLoops;