cupynumeric

If you like cupy, definitely checkout the Multinode Multi-gpu version, cuNumeric: https://github.com/nv-legate/cunumeric Would love to get any feedback from the community.

Parallelism in CS is a bit like security in CS. People know it matters in the abstract senses but you really only get into it if you look for the training specifically. We're getting better at both over time: just as more languages/libraries/etc. are secure by default, more now are parallel by default. There's a ways to go, but I'm glad we didn't do this prematurely, because the technology has improved a lot in the last decade. Look for example at what we can do (safely!) with Rayon in Rust vs (unsafely!) with OpenMP in C++. And there are things even further afield like what I work on [1][2][3]. [1]: https://legion.stanford.edu/ [2]: https://regent-lang.org/ [3]: https://github.com/nv-legate/cunumeric

Legion has been used to develop distributed, drop-in replacements for libraries like NumPy and SciPy Sparse -- https://github.com/nv-legate/cunumeric/, https://github.com/nv-legate/legate.sparse

I am biased because this is my research area, but I have to respectfully disagree. Actor models are awful, and the only reason it's not obvious is because everything else is even more awful. But if you look at e.g., the recent work on task-based models, you'll see that you can have literally sequential programs that parallelize automatically. No message passing, no synchronization, no data races, no deadlocks. Read your programs as if they're sequential, and you immediately understand their semantics. Some of these systems are able to scale to thousands of nodes. An interesting example of this is cuNumeric, which allows you to take sequential Python programs that use NumPy, and by changing one line (the import statement), run automatically on clusters of GPUs. It is 100% pure awesomeness. https://github.com/nv-legate/cunumeric (I don't wo

> GPU support If you haven't tried cuNumeric [1], you really ought to. It's a drop-in NumPy wrapper for distributed GPU acceleration. Would be interesting to see if it works for this. [1]: https://github.com/nv-legate/cunumeric

I know this probably isn't intended for performance, but it would be fun to run this in cuNumeric [1] and see how it scales. [1]: https://github.com/nv-legate/cunumeric

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.