cupynumeric

If you want built-in GPU support (and distributed), you should check out cuNumeric (released by NVIDIA in the last week or so). Also avoids needing to manually specify chunk sizes, like it says in a sibling comment. https://github.com/nv-legate/cunumeric

Try dask Distribute your data and run everything as dask.delayed and then compute only at the end. Also check out legate.numpy from Nvidia which promises to be a drop in numpy replacement that will use all your CPU cores without any tweaks on your part. https://github.com/nv-legate/legate.numpy

You might be interested in Legate [1]. It supports the NumPy interface as a drop-in replacement, supports GPUs and also distributed machines. And you can see for yourself their performance results; they're not far off from hand-tuned MPI. [1]: https://github.com/nv-legate/legate.numpy Disclaimer: I work on the library Legate uses for distributed computing, but otherwise have no connection.

As it turns out, NVIDIA just open sourced a product called Legate which does not just GPUs but distributed as well. Right now it supports NumPy and Pandas but perhaps they'll add others in the future. Just thought this might be up your alley since it works at a higher level than the glorified CUDA in the article. https://github.com/nv-legate/legate.numpy Disclaimer: I work on the project they used to do the distributed execution, but otherwise have no connection with Legate. Edit: And this library was developed by a team managed by one of the original Copperhead developers, in case you're wondering.