CUDA.jl VS cupynumeric

It would also mean learning Julia, but you can write GPU kernels in Julia and then compile for NVidia CUDA, AMD ROCm or IBM oneAPI.

https://juliagpu.org/

I've written CUDA kernels and I knew nothing about it going in.

See https://juliagpu.org/

If you just want to do some numerical code that requires linear algebra and GPU, your best bet would be Julia or Python+JAX.

GitHub - JuliaGPU/CUDA.jl: CUDA programming in Julia.

Should have (2017) in the title.

Indeed cool to program julia directly on the GPU and Julia on GPU and this has further evolved since then, see https://juliagpu.org/

I'm excited to eventually see something like JuliaGPU with support for multiple backends.

That's really cool! Have you looked at CUDA.jl for the Julia language? Maybe you could take some ideas from there. I am pretty sure it does the same thing you do here, and they support any arbitrary code with the limitations that you cannot allocate memory, I/O is disallowed, and badly-typed code(dynamic) will not compile.

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.

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 work on cuNumeric, but I do work on the runtime framework that cuNumeric uses.)

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

Something for the HPC tools category: 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.