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    "info": {
        "author": "Andreas Kloeckner",
        "author_email": "inform@tiker.net",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "Intended Audience :: Other Audience",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: C++",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Scientific/Engineering :: Physics",
            "Topic :: Scientific/Engineering :: Visualization"
        ],
        "description": "PyCUDA lets you access `Nvidia <http://nvidia.com>`_'s `CUDA\n<http://nvidia.com/cuda/>`_ parallel computation API from Python.\nSeveral wrappers of the CUDA API already exist-so what's so special\nabout PyCUDA?\n\n.. image:: https://badge.fury.io/py/pycuda.png\n    :target: http://pypi.python.org/pypi/pycuda\n\n* Object cleanup tied to lifetime of objects. This idiom, often\n  called\n  `RAII <http://en.wikipedia.org/wiki/Resource_Acquisition_Is_Initialization>`_\n  in C++, makes it much easier to write correct, leak- and\n  crash-free code. PyCUDA knows about dependencies, too, so (for\n  example) it won't detach from a context before all memory\n  allocated in it is also freed.\n\n* Convenience. Abstractions like pycuda.driver.SourceModule and\n  pycuda.gpuarray.GPUArray make CUDA programming even more\n  convenient than with Nvidia's C-based runtime.\n\n* Completeness. PyCUDA puts the full power of CUDA's driver API at\n  your disposal, if you wish. It also includes code for\n  interoperability with OpenGL.\n\n* Automatic Error Checking. All CUDA errors are automatically\n  translated into Python exceptions.\n\n* Speed. PyCUDA's base layer is written in C++, so all the niceties\n  above are virtually free.\n\n* Helpful `Documentation <http://documen.tician.de/pycuda>`_ and a\n  `Wiki <http://wiki.tiker.net/PyCuda>`_.\n\nRelatedly, like-minded computing goodness for `OpenCL <http://khronos.org>`_\nis provided by PyCUDA's sister project `PyOpenCL <http://pypi.python.org/pypi/pyopencl>`_.",
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        "keywords": null,
        "license": "MIT",
        "maintainer": null,
        "maintainer_email": null,
        "name": "pycuda-arm-linux",
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        "summary": "pyCUDA for ARM (armv7l) Linux GNUroot Debian on NVIDIA Shield Tablet [Tegra K1] - EXPERIMENTAL: much of cuda driver API works but no cuda runtime API. REQUIRES: Linux for Tegra cuda-6.5-toolkit [.deb package] and libcuda.so.1.1 in /usr/lib (from Jetson TK1 Driver Package) @ https://developer.nvidia.com/linux-tegra-rel-21 ",
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