Hi all, Disclaimer: This isn't strictly about Scipy, but I think the people here are probably the most likely to have something interesting to say about this, as it's about scientific computing with Python. Myself and a student in our lab have written a package that we're using internally to distribute optimisation problems over several CPUs (or GPUs using PyCUDA) over the small cluster of computers in our lab using multiprocessing. It has two parts: some code for distributing work over several computers and over several CPUs/GPUs on those computers. There's no load balancing, we assume that each piece of work is roughly the same, however it does handle using shared memory arrays with numpy automatically (recently discussed on this list and the numpy one). It can use named pipes or IP for communications (we had to use named pipes because of the firewall setup in our lab). The other part of the code is a framework for optimisation routines that can be used with this. So far we've implemented a particle swarm optimisation algorithm which is ideal for this sort of distributed processing because it's very local and so splitting amongst several machines is quite nice. We're also thinking about implementing a genetic algorithm in the same framework. Anyway, getting to the point. We've written this largely for our own use, but it struck us that this might be of interest more widely. The code is fairly simple (a few hundred lines), but there are maybe some non-obvious things in there (at least for some people). We're thinking about releasing it as an independent package, but there's some work involved in that, so we were just looking to see if there was enough interest to make it worth the effort really. Also, it's very likely that our code is less than perfect, so if anyone would be interested in working on it that could be good too. Thanks for any comments! Dan