copperhead
CudaPy
| copperhead | CudaPy | |
|---|---|---|
| 1 | 1 | |
| 207 | 4 | |
| - | - | |
| 0.0 | 0.0 | |
| about 11 years ago | over 8 years ago | |
| Python | Haskell | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
copperhead
-
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:
CudaPy
-
Unifying the CUDA Python Ecosystem
Closest thing to mind is Numba's cuda JIT compilation : https://numba.pydata.org/numba-doc/latest/cuda/index.html
Then you have Cupy : https://github.com/oulgen/CudaPy
But in my opinion, the most future proof solutions are higher level frameworks like Numpy, Jax and Tensorflow. TensorFlow can JIT compile Python functions to GPU (tf.function).
What are some alternatives?
amaranth - A modern hardware definition language and toolchain based on Python
cunumeric - An Aspiring Drop-In Replacement for NumPy at Scale
wgpu-py - Next generation GPU API for Python
CUDA.jl - CUDA programming in Julia.
gtc2017-numba - Numba tutorial for GTC 2017 conference
cudf - cuDF - GPU DataFrame Library
numba - NumPy aware dynamic Python compiler using LLVM
grcuda - Polyglot CUDA integration for the GraalVM