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Architecture: arm64
Depends: nvidia-cuda-toolkit, python3-platformdirs (>= 2.2.0), python3-numpy (>=
1:1.25.0), python3-pytools, python3 (<< 3.14), python3 (>= 3.12~), python3-mako,
python3-numpy-abi9, python3:any, libboost-python1.83.0 (>= 1.83.0), libboost-python1.83.0-py312,
libboost-python1.83.0-py313, libboost-thread1.83.0 (>= 1.83.0), libc6 (>= 2.32),
libcuda1 (>= 450.66) | libcuda.so.1 (>= 450.66), libcurand10, libgcc-s1 (>= 3.0),
libstdc++6 (>= 14)
Description: "Python 3 module to access Nvidia\u2018s CUDA parallel computation\
\ API\n PyCUDA lets you access Nvidia\u2018s CUDA parallel computation API from\
\ Python.\n Several wrappers of the CUDA API already exist\u2013so what\u2019\
s so special about\n PyCUDA?\n * Object cleanup tied to lifetime of objects.\
\ This idiom, often called\n RAII in C++, makes it much easier to write correct,\
\ leak- and crash-free\n code. PyCUDA knows about dependencies, too, so (for\
\ example) it won\u2019t\n detach from a context before all memory allocated\
\ in it is also freed.\n * Convenience. Abstractions like pycuda.driver.SourceModule\
\ and\n pycuda.gpuarray.GPUArray make CUDA programming even more convenient\
\ than\n with Nvidia\u2019s C-based runtime.\n * Completeness. PyCUDA puts\
\ the full power of CUDA\u2019s driver API at your\n disposal, if you wish.\n\
\ * Automatic Error Checking. All CUDA errors are automatically translated\n\
\ into Python exceptions.\n * Speed. PyCUDA\u2019s base layer is written in\
\ C++, so all the niceties\n above are virtually free.\n * Helpful Documentation.\n\
\ .\n This package contains Python 3 modules."
Homepage: http://mathema.tician.de/software/pycuda
Installed-Size: '4014'
Maintainer: Debian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
Package: python3-pycuda
Priority: optional
Recommends: python-pycuda-doc
Section: contrib/python
Source: pycuda
Suggests: python3-matplotlib, python3-opengl, python3-pytest
Version: 2024.1.2~dfsg-2
srcpkg_name: pycuda
srcpkg_version: 2024.1.2~dfsg-2