Thanks for your response Andreas, Yes I was thinking of being able to 
reproduce a serial execution. Although that obviously isn't any where near 
as important as being able to reproduce a piece of work. I've just spent 
three hours writing a reply to you saying your suggestion doesn't work - 
only now realised I had my code set up wrong and had put srand=N instead of 
srand(N). I hate programming!  I just create a user type which has a srand 
field and pass pmap a vector (one for each worker) of such a type - works a 
treat.Thanks for your help.  To get around criticism of independence of 
stream I could just "burn" (as in MCMC runs) a number of draws in each 
stream (criticism seems to be regarding the first N runs). Does anyone have 
any idea what size this burn N should be (can't find any papers on it) 
given the particular MT generator Julia uses? 

Steve

On Thursday, February 26, 2015 at 9:58:40 PM UTC, Andreas Noack wrote:
>
> Would that be to get the exact same variates as the serial execution would 
> create?
>
> 2015-02-26 15:04 GMT-05:00 Steve Kay <[email protected] 
> <javascript:>>:
>
>> Thanks for the comments. - nice to know it's not my usual programming 
>> inadequacies. I like the 
>>
>> for p in workers()
>> @spawnat p srand(seed + p)
>> end
>>
>> idea. It would be even better if instead of resetting the seed it did a 
>> (imaginary) @spawnat p jumpahead(seed,(p*X))  where X was larger than the 
>> number of bootstrap reps for each worker. Here jumpahead(seed,b) took 
>> the state of the random number generator when seed=seed and then moves it 
>> on b steps. Very easy for me to come up with imaginary commands  - way past 
>> my ability to actually program them! 
>>
>> On Thursday, February 26, 2015 at 3:37:19 PM UTC, Steve Kay wrote:
>>>
>>> There is a really nice example of using pmap for parallel bootstrapping 
>>> purposes on http://juliaeconomics.com/2014/06/18/parallel-
>>> processing-in-julia-bootstrapping-the-mle/ .
>>> If you rerun the code however, it's clear that the pmap function does 
>>> not respect the set seed command srand. I've tried various small changes 
>>> but nothing is working (although a lot of nuances around parallel computing 
>>> are a bit above my level). Is it possible to  get pmap to respect the seed 
>>> setting? I hope there is as pmap is superb otherwise.
>>>
>>> Any help much appreciated.
>>>
>>> Best,
>>>
>>> Steve
>>>
>>
>

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