Your parallel computing approach sounds intriguing! Could you provide an example script? I would like to look into this. If you like you could open an issue as a feature request and provide a code snipped there.
I don't have any code handy to share. The project in question was a decade ago and we started with optimization code written in MATLAB. The objective function then turned into a thin wrapper around a python2 script, because that could actually spawn multiple processes unlike the version of MATLAB we were working with. When we started distributing jobs to several machines, we used execnet: https://codespeak.net/execnet/ with hard-coded IP addresses and CPU core counts for each machine. So nothing pretty or particularly useful to share if I could dig it up from my archives.
But as far as illustrating how the optimization framework would need to work to support a vectorized objective function, you can take any existing sample objective function that's written to take N scalar arguments and update it to take N vector arguments, where the length of the vectors is the number of points in the batch to be evaluated. For simple numpy functions, there might not even need to be any changes to the code of the objective function.
But as far as illustrating how the optimization framework would need to work to support a vectorized objective function, you can take any existing sample objective function that's written to take N scalar arguments and update it to take N vector arguments, where the length of the vectors is the number of points in the batch to be evaluated. For simple numpy functions, there might not even need to be any changes to the code of the objective function.