I need to sample a distribution for which the calculation of the log-likelihood is particularly time consuming. To speed-up calculations, it is beneficial to pass to the likelihood mutable data that (1) partially contain pre-computed values and (2) can be modified by the log-likelihood depending on the specific set of parameters passed to each call.
It would be relatively easy to implement this on a sequential (non-parallel) execution of a sampling, since I could attach the mutable data to the object representing the distribution. Problems arise however for parallel processing: in this case I would need to have n copies of the mutable data, one for each task.
One way could be to store n-copies of the data and access them in each log-likelihood call using the threadid(), but as clarified here this is no longer correct.
Another possibility would be to use a lock, but this would cancel much of the benefits of going parallel, as the mutable data are used during all the loglikelihood computation.
I need to sample a distribution for which the calculation of the log-likelihood is particularly time consuming. To speed-up calculations, it is beneficial to pass to the likelihood mutable data that (1) partially contain pre-computed values and (2) can be modified by the log-likelihood depending on the specific set of parameters passed to each call.
It would be relatively easy to implement this on a sequential (non-parallel) execution of a sampling, since I could attach the mutable data to the object representing the distribution. Problems arise however for parallel processing: in this case I would need to have n copies of the mutable data, one for each task.
One way could be to store n-copies of the data and access them in each log-likelihood call using the
threadid(), but as clarified here this is no longer correct.Another possibility would be to use a lock, but this would cancel much of the benefits of going parallel, as the mutable data are used during all the loglikelihood computation.