[Numpy-discussion] numpy.random and multiprocessing

Bruce Southey bsouthey@gmail....
Thu Dec 11 12:00:23 CST 2008

David Cournapeau wrote:
> Sturla Molden wrote:
>> On 12/11/2008 6:10 PM, Michael Gilbert wrote:
>>> Shouldn't numpy (and/or multiprocessing) be smart enough to prevent
>>> this kind of error?  A simple enough solution would be to also include
>>> the process id as part of the seed 
>> It would not help, as the seeding is done prior to forking.
>> I am mostly familiar with Windows programming. But what is needed is a 
>> fork handler (similar to a system hook in Windows jargon) that sets a 
>> new seed in the child process.
>> Could pthread_atfork be used?
> The seed could be explicitly set in each task, no ?
> def task(x):
>     np.random.seed()
>     return np.random.random(x)
> But does this really make sense ?
> Is the goal to parallelize a big sampler into N tasks of M trials, to
> produce the same result as a sequential set of M*N trials ? Then it does
> sound like a trivial task at all. I know there exists libraries 
> explicitly designed for parallel random number generation - maybe this
> is where we should look, instead of using heuristics which are likely to
> be bogus, and generate wrong results.
> cheers,
> David
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This is not sufficient because you can not ensure that the seed will be 
different every time task() is called.

A major part of the problem here is treating a parallel computing 
problem as a serial computing problem.  The streams must be independent 
across threads especially avoiding cross-correlation of streams (another 
gotcha) between threads.  It is up to the user to implement a 
thread-safe solution such as using a single stream that is used by all 
threads or force the different threads to start at different states. The 
only thing that Numpy could do is provide a parallel pseudo-random 
number generator.


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