CudaPy
numba
CudaPy | numba | |
---|---|---|
1 | 1 | |
4 | 5 | |
- | - | |
0.0 | 0.0 | |
over 8 years ago | 1 day ago | |
Haskell | Python | |
MIT License | BSD 2-clause "Simplified" License |
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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).
numba
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Unifying the CUDA Python Ecosystem
that project might be abandoned but this strategy is used in nvidia and nvidia adjacent projects (through llvm):
https://github.com/rapidsai/cudf/blob/branch-0.20/python/cud...
https://github.com/gmarkall/numba/blob/master/numba/cuda/com...
>but we also need high level expressibility that doesn't require writing kernels in C
the above are possible because C is actually just a frontend to PTX
https://docs.nvidia.com/cuda/parallel-thread-execution/index...
fundamentally you are not going to ever be able to have a way to write cuda kernels without thinking about cuda architecture anymore so than you'll ever be able to write async code without thinking about concurrency.
What are some alternatives?
cunumeric - An Aspiring Drop-In Replacement for NumPy at Scale
CUDA.jl - CUDA programming in Julia.
cudf - cuDF - GPU DataFrame Library
grcuda - Polyglot CUDA integration for the GraalVM
wgpu-py - Next generation GPU API for Python
copperhead - Data Parallel Python
amaranth - A modern hardware definition language and toolchain based on Python
gtc2017-numba - Numba tutorial for GTC 2017 conference