Consistently generating same random sequence with for and parfor loop

Question

sulav duwal 2017-10-30

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/364063-consistently-generating-same-random-sequence-with-for-and-parfor-loop

编辑： Robert 2017-11-6

Dear all,

I encountered a problem, when I was running monte-carlo simulation. In order to speed up the simulation, I decided to change 'for' loop to 'parfor'. In order to debug and to later check the details of trajectory, I fixed the seed for random number generator within the loop. To my surprise, though the seeds for random number generator in 'for' and 'parfor' loop are same, the random number sequence are completely different. This creates a problem, since I cannot check the trajectories in 'parfor'.

% sample code with 'for' loop
rng(5)
seedForSimulation = randi(100,1,10);
randomNrs = NaN(1,10);
Seeds = NaN(1,10);
for i = 1:10
    rng(SeedForSimulation(1,i));
    Seeds(i) = SeedForSimulation(1,i);
    randomNrs(i) = randi(10);
end   
display(Seeds)
display(randomNrs)

The result that I get is :

Seeds = 23 88 21 92 49 62 77 52 30 19
randomNrs = 6 7 1 9 4 1 10 9 7 1

Whereas the same code with 'parfor'

% code
rng(5)
seedForSimulation = randi(100,1,10);
randomNrs = NaN(1,10);
Seeds = NaN(1,10);
parfor i = 1:10
    rng(SeedForSimulation(1,i));
    Seeds(i) = SeedForSimulation(1,i);
    randomNrs(i) = randi(10);
end   
display(Seeds)
display(randomNrs)

The result that I get :

Seeds = 23 88 21 92 49 62 77 52 30 19
randomNrs = 5 4 9 5 4 4 10 7 8 4

Though the seeds are same within 'for' and 'parfor', I get different random numbers. Can someone explain me what is Matlab doing ? How can I circumvent the problem.

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Answer 1

Robert 2017-10-30

1
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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/364063-consistently-generating-same-random-sequence-with-for-and-parfor-loop#answer_288509

编辑：Robert 2017-10-30

在 MATLAB Online 中打开

You can produce the same random numbers in a parfor loop if you explicitly set the random number generator type to be the same in the for and parfor loops.

You can do this when you set the seed or before the loop.

parfor i = 1:10
  rng(seedForSimulation(1, i), 'twister');
  Seeds(1, i) = seedForSimulation(1, i);
  randomNrs(1, i) = randi(10);
end

This will return the exact same result if you replace parfor with for. You could also use:

spmd % run once on each parallel worker
  rng(0, 'twister')
end
parfor i = 1:10
  rng(seedForSimulation(1, i)); % no need to specify again
  Seeds(1, i) = seedForSimulation(1, i);
  randomNrs(1, i) = randi(10);
end

This will also return the same result as would the for loop.

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Robert 2017-10-30

编辑：Robert 2017-10-30

在 MATLAB Online 中打开

My new test code

rng default
seedForSimulation = randi(10,1,10);
rng_source = 'twister'; % or 'combRecursive' or any other
% not needed if using 'twister' since that is default in base workspace
rng(0, rng_source)
% not needed if 'combRecursive' since that is default for parallel workers
spmd
  rng(0, rng_source)
end
randomNrs = NaN(2,10);
Seeds = NaN(2,10);
for i = 1:10
  rng(seedForSimulation(1,i));
  Seeds(1, i) = seedForSimulation(1,i);
  randomNrs(1, i) = randi(10);
end
parfor i = 1:10
  rng(seedForSimulation(1,i));
  Seeds(2, i) = seedForSimulation(1,i);
  randomNrs(2, i) = randi(10);
end
display(Seeds)
display(randomNrs)

whose output is

Seeds =
   9    10     2    10     7     1     3     6    10    10
   9    10     2    10     7     1     3     6    10    10
randomNrs =
   1     8     5     8     1     5     6     9     8     8
   1     8     5     8     1     5     6     9     8     8