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 |
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.
CUDAdrv.jl
-
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
-
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