CUDA Python: Performance meets Productivity
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CUDA Python is the home for accessing NVIDIA’s CUDA platform from Python. It consists of multiple components:
cuda.core: Pythonic access to CUDA Runtime and other core functionalities
cuda.bindings: Low-level Python bindings to CUDA C APIs
cuda.cooperative: A Python package providing CCCL’s reusable block-wide and warp-wide device primitives for use within Numba CUDA kernels
cuda.parallel: A Python package for easy access to CCCL’s highly efficient and customizable parallel algorithms, like sort, scan, reduce, transform, etc, that are callable on the host
numba.cuda: Numba’s target for CUDA GPU programming by directly compiling a restricted subset of Python code into CUDA kernels and device functions following the CUDA execution model.
For access to NVIDIA CPU & GPU Math Libraries, please refer to nvmath-python.
CUDA Python is currently undergoing an overhaul to improve existing and bring up new components. All of the previously available functionalities from the cuda-python package will continue to be available, please refer to the cuda.bindings documentation for installation guide and further detail.
cuda-python as a metapackage
cuda-python is now a metapackage that contains a collection of subpackages. Each subpackage is versioned independently, allowing installation of each component as needed.
Subpackage: cuda.core
The cuda.core package offers idiomatic, pythonic access to CUDA Runtime and other functionalities.
The goals are to
Provide idiomatic (“pythonic”) access to CUDA Driver, Runtime, and JIT compiler toolchain
Focus on developer productivity by ensuring end-to-end CUDA development can be performed quickly and entirely in Python
Avoid homegrown Python abstractions for CUDA for new Python GPU libraries starting from scratch
Ease developer burden of maintaining and catching up with latest CUDA features
Flatten the learning curve for current and future generations of CUDA developers
Subpackage: cuda.bindings
The cuda.bindings package is a standard set of low-level interfaces, providing full coverage of and access to the CUDA host APIs from Python.
The list of available interfaces are:
CUDA Driver
CUDA Runtime
NVRTC
nvJitLink
NVVM
cuFile
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