copperhead
cunumeric
| copperhead | cunumeric | |
|---|---|---|
| 1 | 9 | |
| 207 | 596 | |
| - | 0.2% | |
| 0.0 | 8.5 | |
| about 11 years ago | 10 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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copperhead
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Unifying the CUDA Python Ecosystem
Oh that sounds interesting. Do you know what happened to it?
I think I found it here: https://github.com/bryancatanzaro/copperhead
But I'm not sure what the state is. Looks dead (last commit 8 years ago). Probably just a proof of concept. But why hasn't this been continued?
Blog post and example:
cunumeric
- Announcing Chapel 1.32
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Is Parallel Programming Hard, and, If So, What Can You Do About It? [pdf]
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.)
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GPT in 60 Lines of NumPy
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
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Dask – a flexible library for parallel computing in Python
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
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Julia is the better language for extending Python
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
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Learning more about HPC as a python guy
Something for the HPC tools category: https://github.com/nv-legate/legate.numpy
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Unifying the CUDA Python Ecosystem
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.
- Legate NumPy: An Aspiring Drop-In Replacement for NumPy at Scale
What are some alternatives?
amaranth - A modern hardware definition language and toolchain based on Python
cupy - NumPy & SciPy for GPU
wgpu-py - Next generation GPU API for Python
CudaPy - CudaPy is a runtime library that lets Python programmers access NVIDIA's CUDA parallel computation API.
gtc2017-numba - Numba tutorial for GTC 2017 conference
CUDA.jl - CUDA programming in Julia.
numba - NumPy aware dynamic Python compiler using LLVM
cudf - cuDF - GPU DataFrame Library
legate.pandas - An Aspiring Drop-In Replacement for Pandas at Scale
grcuda - Polyglot CUDA integration for the GraalVM