parfor problem (broadcast variable)
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Hi, I'm trying to use "parfor" in my MATLAB script.
It goes well without error, but It shows no significant speed boost compared to for loop.
And I got this warning message:
"the entire array ('A_Data' , 'A_ref') is a broadcast variable. This might result in unnecessary communication overhead."
How can I deal with this problem?
Note that:
A_Data: 1710203x5 double
A_ref: 5760x5 double
Please suggest the solutions.
% load 'A_Data' and 'A_ref' matrix
load dataq.mat
% preallocation ('theta' ,'b_ref')
k=length(A_ref);
theta=zeros(k,5);
b_ref=zeros(k,1);
% regression
poolobj = parpool(8);
parfor i=1:k
W = sqrt( ...
exp( ...
-( ...
( ( A_Data(:,2)-A_ref(i,2) ) / 0.6 ).^2+ ...
( ( A_Data(:,3)-A_ref(i,3) ) / 0.6 ).^2+ ...
( ( A_Data(:,4)-A_ref(i,4) ) / 0.6 ).^2+ ...
( ( A_Data(:,5)-A_ref(i,5) ) / 0.6 ).^2 ...
) /2 ...
) ...
);
theta(i,:)=regress(W.*b_Data,repmat(W,1,5).*A_Data);
b_ref(i,1)=A_ref(i,:)*theta(i,:)';
end
delete(poolobj);
回答（1 个）
There's never any gaurantee parfor will be faster, but I would modify the code as follows,
B_Data=A_Data(:,2:5)/(0.6*2);
B_ref=A_ref(:,2:5)/(0.6*2);
parfor i=1:k
W = exp(sum( -( B_Data-B_ref(i,:) ) ).^2 ,2) ;
theta(i,:)=regress(W.*b_Data,W.*A_Data);
end
b_ref=A_ref*theta.';
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2020-9-18
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