why the caculation results is different for parfor-loop and for-loop?
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load('matlab.mat')
for i =1:1
b_OLS(i) = X_*y;
end
parfor i =1:1
b_OLS_(i) = X_*y;
end
error = b_OLS_-b_OLS
The results is:
error =
1.8190e-11
why the caculation results is different for parfor-loop and for-loop?
1 件のコメント
Stephen23
2021 年 9 月 27 日
Because the associative laws of algebra do not generally hold for floating-point numbers.
回答 (1 件)
Matt J
2021 年 9 月 23 日
編集済み: Matt J
2021 年 9 月 27 日
Probably because, with a parpool active, the matrix multiplication code cannot multithread the operation in precisely the same way. The vectors are split into parallel blocks of one size if a parpool is open and another size if not.
1 件のコメント
Edric Ellis
2021 年 9 月 27 日
In particular, by default for process-based parpool, the workers run in single-computational-thread mode. This can definitely result in slightly different results compared to multithreaded mode. You can use maxNumCompThreads(1) to put the client in single-computational-thread mode to check. Like this:
A = rand(1,10000);
B = rand(10000,1);
maxNumCompThreads(4);
c1 = A*B;
maxNumCompThreads(1);
c2 = A*B;
c1-c2
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