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 >
