CUDAdrv.jl
CudaPy
| CUDAdrv.jl | CudaPy | |
|---|---|---|
| 1 | 1 | |
| 64 | 4 | |
| - | - | |
| 0.0 | 0.0 | |
| almost 4 years ago | over 8 years ago | |
| Julia | Haskell | |
| GNU General Public License v3.0 or later | MIT License |
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CUDAdrv.jl
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Unifying the CUDA Python Ecosystem
here is writing a similar kernel in python with numba: https://github.com/ContinuumIO/gtc2017-numba/blob/master/4%2...
I think the contrast is less about the language, and more about the scope and objective of the project. the blog is describing low-level interfaces in python - probably more comparable is the old CUDAdrv.jl package (now merged into CUDA.jl): https://github.com/JuliaGPU/CUDAdrv.jl/blob/master/examples/...
CudaPy
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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?
gtc2017-numba - Numba tutorial for GTC 2017 conference
cunumeric - An Aspiring Drop-In Replacement for NumPy at Scale
copperhead - Data Parallel Python
CUDA.jl - CUDA programming in Julia.
wgpu-py - The next generation GPU API for Python
cudf - cuDF - GPU DataFrame Library
amaranth - A modern hardware definition language and toolchain based on Python
grcuda - Polyglot CUDA integration for the GraalVM
numba - NumPy aware dynamic Python compiler using LLVM