Comments (4)
This is of high interest to me (my model runs are quite expensive)!
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Because our samplers also do internal parallelization, parallelizing the chains as well requires some changes to the code structure, so this wonβt happen in the next months.
So far, the best solution is to do the workaround (see above), starting 3 independent R instances
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That's exactly what I'm doing. Thanks again for your feedback!
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Tankred, can you have a look at this and not implement, but just think about whether we can do this in a stable, non too-hacky way? If this doesn't seem save, I'd prefer it to let users do this by hand.
I guess the problem is that we have currently attached the parallelization to the BayesianSetup, which also seems important in some way, because only this ensures that people with heavy models have full control of hd io things. I'm not sure if this could be used by the runMCMC to parallelize the chains, I guess not.
So we would have to create a second parallelization option in runMCMC ... could do that, but doesn't feel stable / save.
Maybe we need to rethink the entire parallelization concept. In any case, at the moment, I just want to brainstorm about what the options are.
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Related Issues (20)
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