Client and Workers

If it is necessary to generate the same stream of numbers in the client and workers, you can set one to match the other. You can set the generator algorithm and seed using rng.

For example, you might run a script as a batch job on a worker, and need the same generator or sequence as the client. Suppose you start with a script file named randScript1.m that contains the line:

R = rand(1,4);

You can run this script in the client, and then as a batch job on a worker. Notice that the default generated random number sequences in the results are different.

randScript1; % In client
R

R =
    0.8147    0.9058    0.1270    0.9134

parallel.defaultClusterProfile('Processes')
c = parcluster();
j = batch(c,'randScript1'); % On worker
wait(j);load(j);
R

R =
    0.1349    0.6744    0.9301    0.5332

For identical results, you can set the client and worker to use the same generator and seed. Here, the file randScript2.m contains the following code:

rng(1,'Threefry');
R = rand(1,4);

Now, run the new script in the client and on a worker:

randScript2; % In client
R

R =
    0.1404    0.8197    0.1073    0.4131

j = batch(c,'randScript2'); % On worker
wait(j); load(j);
R

R =
    0.1404    0.8197    0.1073    0.4131

To reset the settings of the random number generator to their default values in the client and on the worker, you can add this code to the end of randScript2.m.

rng('default')

Different Workers

By default, each worker in a cluster working on the same job has an independent random number stream. If rand, randi, or randn are called in parallel, each worker produces a unique sequence of random numbers.

This example uses two workers in a parallel pool to show they generate unique random number sequences.

p = parpool(2);
spmd
    R = rand(1,4); % Different on each worker
end
R{1},R{2}

ans = 
    0.1349    0.6744    0.9301    0.5332
ans =
    0.6383    0.5195    0.1398    0.6509

delete(p)

If you need all workers to generate the same sequence of numbers, you can set each worker to use the same generator settings:

p = parpool(2);
spmd
    rng(0,'Philox'); % Default seed 0.
    R = rand(1,4); % Same on all workers
end
R{1},R{2}

ans =
    0.3655    0.6975    0.1789    0.4549
ans =
    0.3655    0.6975    0.1789    0.4549

delete(p)

If you need to control the random numbers at each iteration of a parfor-loop, see Repeat Random Numbers in parfor-Loops.

Normally Distributed Random Numbers

If you are working with normally distributed random numbers using the randn function, you can use the same methods as above using RandStream to set the generator type, seed, and normal transformation algorithm on each worker and the client.

For example, suppose the file randScript3.m contains the code:

stream = RandStream('Threefry','Seed',0,'NormalTransform','Inversion');
RandStream.setGlobalStream(stream);
R = randn(1,7)

R =
    -0.3479    0.1057    0.3969    0.6544   -1.8228    0.9587    0.5360