Parfor seems slower than for loop

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federico nutarelli 2022-11-18

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https://ww2.mathworks.cn/matlabcentral/answers/1855473-parfor-seems-slower-than-for-loop

评论： federico nutarelli 2022-11-21

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Hi all,

I am trying to parallelize an operation in MATLAB. The MWE goes like this:

clear
A=rand(5000,3000);
B=int8(rand(5000,3000)>0.5);
lambda_tol=0.00000000000000004;
N=10;
%A='/Users/federiconutarelli/Desktop/Paper_Samuel/cd_2004.csv';
G=15;
lambda_tol_vector= zeros(G,1);
conto = 1;
for h=-G:0.1:G
    lambda_tol_vector(conto)=2^(h);
    conto = conto+1;
end
M=1;
tic
tol = 1e-9;
parfor (k = 1:size(lambda_tol_vector,1),M)
    lambda_tol = lambda_tol_vector(k);
    fprintf('Completion using nuclear norm regularization... \n');
    [CompletedMat,objective,flag] = matrix_completion_nuclear_GG_alt(A.*double(B),double(B),N,lambda_tol,tol);
    if flag==1
        CompletedMat=zeros(size(A));
    end
end
toc

Now, what I would expect is that when M>1 the time taken is lower than when M=1. However the decrease in time is not very sensitive. Why is like that?

Furthermore, I have noticed that, after a while that I do not launch the parfor, the latter takes on a while displaying the ollowing message before starting the loop:

Starting parallel pool (parpool) using the 'local' profile ...
Preserving jobs with IDs: 1 because they contain crash dump files.
You can use 'delete(myCluster.Jobs)' to remove all jobs created with profile local. To create 'myCluster' use 'myCluster = parcluster('local')'.
Connected to the parallel pool (number of workers: 6).

Is there a way to avoid this message to appear and go directly to the parfor loop?

Thank you

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Stephen23 2022-11-21

Expanding on Ayush's advice to vectorize, you will find more advice on how to write faster MATLAB code here:

https://www.mathworks.com/help/matlab/matlab_prog/techniques-for-improving-performance.html

From the short look I took now, you will find a number of those recommendations apply to your code.

As Walter Roberson already commented in your other thread on this topic, many MATLAB operations are inherently multi-threaded, so simply jumping on the parallel-bandwagon is not a "universal speed up" that many beginners image it to be. In contrast, understanding how arrays are stored, careful testing, and following MATLAB best-practice are much more likely to have a positive impact on code performance.

federico nutarelli 2022-11-21

@Stephen23 thank you a lot for improving on the questionn. I will surely pay more attention on carefully applying MATLAB bes-practice addvice and get deeper on the link about code improvement.

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