Convert a for loop to parfor loop

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Amita Giri
Amita Giri 2020-2-9
评论： Amita Giri 2020-2-10
Hi,
In order to make my code faster, I am trying to convert for loop to parfor loop.
The original subpart of my code is (subject to error)
SNR_ind = -5:2.5:10;
run = 100;
count = 1;
for SNR_ind = SNR
error_NP = [];
error_SPVP = [];
parfor i = 1:run
%% Measurement
[S_IC,S_IT,W_T,W_C] = noise_gen(SNR_ind,SIR,A_I,S_IC1,S_IT1,I,N_I,Avg_eeg_S.avg,t_index);
[Avg_eeg_IC,~] = source_modeling(vol,elec,d_I_loc,t_index,S_IC); % With new S_IC,S_IT
[Avg_eeg_IT,dip_posm] = source_modeling(vol,elec,d_I_loc,t_index,S_IT);
X_T = Avg_eeg_S.avg + Avg_eeg_IT.avg + W_T;
X_C = Avg_eeg_IC.avg + W_C;
%% Inverse localization
[error] = Null_proj(leadfield_eeg_scan,N_S,N_I,X_C,X_T,zplane,mri_resliced,d_S_loc);
error_NP = [error_NP error];
[error] = SPVP_proj(leadfield_eeg_scan,N_S,N_I,X_C,X_T,zplane,mri_resliced,d_S_loc);
error_SPVP = [error_SPVP error];
end
Eerror_NP(count,:) = error_NP;
Eerror_SPVP(count,:) = error_SPVP;
count = count+1;
end
I have used parfor in inner loop as the inner loop will run more times than the outer loop.
I am getting error - " Error: The temporary variable 'S_IC' must be set before it is used. For more information, see Parallel for Loops in MATLAB, "Uninitialized Temporaries"."
I do understand the problem that variable S_IC need to be used in the very next statement it is calculated. But I don't know how to work around. I've read the documentation, but i can't figured out how to convert into a parfor loop. How should I use parfor in this context?
  2 个评论
Walter Roberson
Walter Roberson 2020-2-9
error_NP = [error_NP error];
Do not do that. Assign to a row or column instead. Or if there is a different output size then assign into a cell array and convert to numeric vector afterwards.
Amita Giri
Amita Giri 2020-2-10
Thanks Walter, it gives me a whole insight of how to write a series code to parallel.

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Amita Giri
Amita Giri 2020-2-10
编辑：Amita Giri 2020-2-10
I figured out, how to solve serial for to parfor. The variable who are causing the dependency in the loop. I calculated them in a prior loop and called them afterwards. Also, it's better to use cells instead of arrays in context to parallel computing. Parfor decreased my computation time by 1/4. This is really nice to learn. The revised code of my earlier code is as follows.
SNR = -5:2.5:10;
run = 100;
count = 1;
for SNR_ind = SNR
parfor i = 1:run
[S_IC,S_IT,W_T,W_C] = noise_gen(SNR_ind,SIR,A_I,S_IC_initial,S_IT_initial,I,N_I,Avg_eeg_S.avg,t_index);
S_IC1{1,i} = S_IC;
S_IT1{1,i} = S_IT;
W_T1{1,i} = W_T;
W_C1{1,i} = W_C;
end
S_IC2{count,1} = S_IC1;
S_IT2{count,1} = S_IT1;
W_T2{count,1} = W_T1;
W_C2{count,1} = W_C1;
count = count+1;
end
count = 1;
for SNR_ind = SNR
parfor i = 1:run
[Avg_eeg_IC,~] = source_modeling(vol,elec,d_I_loc,t_index,S_IC2{count,1}{1,i}); % With new S_IC,S_IT
Avg_eeg_IC1{1,i} = Avg_eeg_IC.avg;
[Avg_eeg_IT,~] = source_modeling(vol,elec,d_I_loc,t_index,S_IT2{count,1}{1,i}); % With new S_IC,S_IT
Avg_eeg_IT1{1,i} = Avg_eeg_IT.avg;
end
Avg_eeg_IC2{count,1} = Avg_eeg_IC1;
Avg_eeg_IT2{count,1} = Avg_eeg_IT1;
count = count+1;
end
count = 1;
for SNR_ind = SNR
for i = 1:run
X_T1{1,i} = Avg_eeg_S.avg + Avg_eeg_IT2{count,1}{1,i}+ W_T2{count,1}{1,i};
X_C1{1,i} = Avg_eeg_IC2{count,1}{1,i}+ W_C2{count,1}{1,i};
end
X_T{count,1} = X_T1;
X_C{count,1} = X_C1;
count = count+1;
end
count = 1;
for SNR_ind = SNR
parfor i = 1:run
i
close all
%% Inverse localization
[error_NP{1,i}] = Null_proj(leadfield_eeg_scan,N_S,N_I,X_C{count,1}{1,i},X_T{count,1}{1,i},zplane,mri_resliced,d_S_loc);
[error_SPVP{1,i}] = SPVP_proj(leadfield_eeg_scan,N_S,N_I,X_C{count,1}{1,i},X_T{count,1}{1,i},zplane,mri_resliced,d_S_loc);
end
Eerror_NP{count,:} = cell2mat(error_NP);
Eerror_SPVP{count,:} = cell2mat(error_SPVP);
count = count+1;
end
  2 个评论
Walter Roberson
Walter Roberson 2020-2-10
The first line should assign to SNR instead of SNR_ind
Amita Giri
Amita Giri 2020-2-10
Yeah, alright. I have incorporated the changes. Thanks

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