As described in Control Random Number Streams on Workers, each
worker in a cluster working on the same job has an independent random number generator
stream. By default, therefore, each worker in a pool, and each iteration in a
parfor-loop has a unique, independent set of random numbers.
Subsequent runs of the
parfor-loop generate different numbers.
parfor-loop, you cannot control what sequence the iterations
execute in, nor can you control which worker runs which iterations. So even if you reset
the random number generators, the
parfor-loop can generate the same
values in a different sequence.
To reproduce the same set of random numbers in a
time the loop runs, you must control random generation by assigning a particular
substream for each iteration.
First, create the stream you want to use, using a generator that supports substreams.
Creating the stream as a
parallel.pool.Constant allows all
workers to access the stream.
sc = parallel.pool.Constant(RandStream('Threefry'))
parfor-loop, you can set the substream index by the loop
index. This ensures that each iteration uses its particular set of random numbers,
regardless of which worker runs that iteration or what sequence iterations run
r = zeros(1,16); parfor i = 1:16 stream = sc.Value; % Extract the stream from the Constant stream.Substream = i; r(i) = rand(stream); end r
r = Columns 1 through 8 0.3640 0.8645 0.0440 0.7564 0.5323 0.8075 0.2145 0.9128 Columns 9 through 16 0.4057 0.0581 0.5515 0.4347 0.3531 0.4677 0.8287 0.2312