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Variable in parfor cannot be classified, error not shown in editor

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Hey everyone I've been looking at parfor and going through some previously given answers and documentation but I'm having a hard time to figure out what my mistake is.
Error: The variable CFxL_Norm in a parfor cannot be classified.
I do assume that CFxLconst_Norm will give the same error. In my script I first assign 2 empty cell-matrix of size (XX,10) and each cell will contain a matrix (501x401x401), except in the last column the cells will contain 1 simple number.
normalisePerc=[0.2, 0.5, 0.8, 1, 0.01, 0.05, 0.1, 0.3, 0.4, 0.6, 0.7, 0.9]; %values can be changed later on
Lnlp=length(normalisePerc); %Length of the normalise percentage array
CFxL_Norm=cell(Lnlp,8+2); % CFxLight400nm + normaliseperc
CFxLconst_Norm=cell(Lnlp,8+2); % CFxLight400nm + normaliseperc
some other code and then this
A=Ef_Norm{9}; %%% assign variable as temporary for parfor, throwaway afterwards
%Lnlp=length(normalisePerc); %Same, to not use length but a fixed constant
parfor kk=1:Lnlp
fprintf('number %i of %i cycles for normalising \n', kk, Lnlp);
NLP=normalisePerc(kk); %get value from array
NLPinv=1/NLP; %allows to use multiplication in a few places for an increase in speed
D=A.*NLPinv;
D(D > 1) = 2./(1+1./D(D > 1));
for tt=1:8
fprintf('number %i of %i steps for normalising \n', tt+(kk-1)*8, Lnlp*8);
tic
B=Ef_Norm{tt}.*NLPinv; %Gives a warning about overhead but not causing problems
B(B > 1) = 2./(1+1./B(B > 1));
for qq=1:lyq2 %Would have prefered to use parfor for this loop, lyq2=401. Matlab recommends outer loops only
CFs=Cafluo_Norm{tt}(:,:,qq)'; %Gives a warning about overhead but not causing problems
CFxL_Norm{kk,tt}(:,:,qq)=CFs.*B(:,:,qq);
CFxLconst_Norm{kk,tt}(:,:,qq)=CFs.*D(:,:,qq);
end
fprintf('Time needed for this step was: \n');
toc
end
tt=9;
for qq=1:lyq2 %Also would have prefered to use parfor for this loop but Matlab recommends outer loops only.
tmp1=Cafluo_Norm{tt}(:,:,qq)'.*D(:,:,qq);
CFxL_Norm{kk,tt}(:,:,qq)=tmp1;
CFxLconst_Norm{kk,tt}(:,:,qq)=tmp1;
end
end
tt=10;
tmpnlp=num2cell(normalisePerc');
CFxL_Norm(:,10)=tmpnlp;
CFxLconst_Norm(:,tt)=tmpnlp;
clear tt kk NLP A B D NLPinv qq CFs tmp1 tmpnlp
fprintf('\n \n \n');
I don't see any of the problems regarding things, like addressing the same cell at the same time or even multiple times. I am also pretty sure the variables are independent of each other. Thus I don't really understand why I'm still getting this error and what I'm doing wrong.

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Accepted Answer

Edric Ellis
Edric Ellis on 29 Jul 2020
The code you've shown there isn't complete enough for us to attempt to run and see the error you're encountering. It would be helpful if you could simplify the code to the point where we can try and run it as a for loop before trying a parfor loop.
That said, I think you're hitting one of the restrictions of assigning into sliced variables with nested for loops inside parfor. This doc page has the details. I think the relevant restriction is:
  • Required (static): If you use a nested for-loop to index into a sliced array, you cannot use that array elsewhere in the parfor-loop.
In your code, you've got multiple cases where you're assigning into the sliced outputs. I would recommend trying to change things so that you have only a single assignment statement into your sliced outputs.

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Bob photonics
Bob photonics on 29 Jul 2020
I tried a few changes based on what I understood about slicing but it didn't resolve the problem gives me the same error again:
Error: The variable CFxL_Norm in a parfor cannot be classified.
See Parallel for Loops in MATLAB, "Overview".
A=Ef_Norm{9}; %%% assign variable as temporary for parfor, throwaway afterwards
parfor kk=1:Lnlp
fprintf('number %i of %i cycles for normalising \n', kk, Lnlp);
NLP=normalisePerc(kk); %get value from array
NLPinv=1/NLP; %allows to use multiplication in a few places for an increase in speed
D=A.*NLPinv;
D(D > 1) = 2./(1+1./D(D > 1));
for tt=1:8
fprintf('number %i of %i steps for normalising \n', tt+(kk-1)*8, Lnlp*8);
tic
B=Ef_Norm{tt}.*NLPinv; %Gives a warning about overhead
B(B > 1) = 2./(1+1./B(B > 1));
AAA=zeros(lzq2,lxq2,lyq2); %CHANGE TO ADD THIS, temporary variable
AAA2=zeros(lzq2,lxq2,lyq2); %CHANGE TO ADD THIS, temporary variable
for qq=1:lyq2
CFs=Cafluo_Norm{tt}(:,:,qq)'; %Gives a warning about overhead
AAA(:,:,qq)=CFs.*B(:,:,qq); %Introduced to remove slicing and indexing issues
AAA2(:,:,qq)=CFs.*D(:,:,qq); %Introduced to remove slicing and indexing issues
end
CFxL_Norm{kk,tt}=AAA; %Assign here to actual variable
CFxLconst_Norm{kk,tt}=AAA2; %Assign here to actual variable
fprintf('Time needed for this step was: \n');
toc
end
tt=9;
AAA3=zeros(lzq2,lxq2,lyq2); %Same changes repeated as before
AAA4=zeros(lzq2,lxq2,lyq2); %same change
for qq=1:lyq2
tmp1=Cafluo_Norm{tt}(:,:,qq)'.*D(:,:,qq);
AAA3(:,:,qq)=tmp1; %same change as before
AAA4(:,:,qq)=tmp1; %same change
end
CFxL_Norm{kk,tt}=AAA3; %same change
CFxLconst_Norm{kk,tt}=AA4; %Same change
end
tt=10;
tmpnlp=num2cell(normalisePerc');
CFxL_Norm(:,10)=tmpnlp;
CFxLconst_Norm(:,tt)=tmpnlp;
Walter Roberson
Walter Roberson on 29 Jul 2020
In the below, I have made the relevant changes only for CFxL_Norm, without looking at the other variables. You probably need to make similar changes for CFxLconst_Norm .
The key here is to use temporary variables to accumulate all partial results, and then have a single assignment statement at the end that changes everything having to do with the variable indexed by the parfor index.
A=Ef_Norm{9}; %%% assign variable as temporary for parfor, throwaway afterwards
parfor kk=1:Lnlp
fprintf('number %i of %i cycles for normalising \n', kk, Lnlp);
NLP=normalisePerc(kk); %get value from array
NLPinv=1/NLP; %allows to use multiplication in a few places for an increase in speed
D=A.*NLPinv;
D(D > 1) = 2./(1+1./D(D > 1));
AAAt = cell(1,9);
for tt=1:8
fprintf('number %i of %i steps for normalising \n', tt+(kk-1)*8, Lnlp*8);
tic
B=Ef_Norm{tt}.*NLPinv; %Gives a warning about overhead
B(B > 1) = 2./(1+1./B(B > 1));
AAA=zeros(lzq2,lxq2,lyq2); %CHANGE TO ADD THIS, temporary variable
AAA2=zeros(lzq2,lxq2,lyq2); %CHANGE TO ADD THIS, temporary variable
for qq=1:lyq2
CFs=Cafluo_Norm{tt}(:,:,qq)'; %Gives a warning about overhead
AAA(:,:,qq)=CFs.*B(:,:,qq); %Introduced to remove slicing and indexing issues
AAA2(:,:,qq)=CFs.*D(:,:,qq); %Introduced to remove slicing and indexing issues
end
AAAt{tt}=AAA; %Assign here to actual variable
CFxLconst_Norm{kk,tt}=AAA2; %Assign here to actual variable
fprintf('Time needed for this step was: \n');
toc
end
tt=9;
AAA3=zeros(lzq2,lxq2,lyq2); %Same changes repeated as before
AAA4=zeros(lzq2,lxq2,lyq2); %same change
for qq=1:lyq2
tmp1=Cafluo_Norm{tt}(:,:,qq)'.*D(:,:,qq);
AAA3(:,:,qq)=tmp1; %same change as before
AAA4(:,:,qq)=tmp1; %same change
end
AAAt{tt}=AAA3; %same change
CFxLconst_Norm{kk,tt}=AA4; %Same change
CFxL_Norm(kk,:) = AAAt;
end
tt=10;
tmpnlp=num2cell(normalisePerc');
CFxl_Norm(:,tt)=tmpnlp;
CFxLconst_Norm(:,tt)=tmpnlp;
Bob photonics
Bob photonics on 6 Aug 2020
@walter Robison
Sorry it looks like my reply didn't get posted.
Nonetheless thank you very much as this absolutely solved my problem.

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