PyCUDA: Pythonic Access to CUDA, with Arrays and Algorithms

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PyCUDA lets you access Nvidia <https://nvidia.com>‘s CUDA <https://nvidia.com/cuda/> parallel computation API from Python. Several wrappers of the CUDA API already exist-so what’s so special about PyCUDA?

• Object cleanup tied to lifetime of objects. This idiom, often called RAII <https://en.wikipedia.org/wiki/Resource_Acquisition_Is_Initialization>_ in C++, makes it much easier to write correct, leak- and crash-free code. PyCUDA knows about dependencies, too, so (for example) it won’t detach from a context before all memory allocated in it is also freed.

• Convenience. Abstractions like pycuda.driver.SourceModule and pycuda.gpuarray.GPUArray make CUDA programming even more convenient than with Nvidia’s C-based runtime.

• Completeness. PyCUDA puts the full power of CUDA’s driver API at your disposal, if you wish. It also includes code for interoperability with OpenGL.

• Automatic Error Checking. All CUDA errors are automatically translated into Python exceptions.

• Speed. PyCUDA’s base layer is written in C++, so all the niceties above are virtually free.

• Helpful Documentation <https://documen.tician.de/pycuda>_.

Relatedly, like-minded computing goodness for OpenCL <https://www.khronos.org/registry/OpenCL/>_ is provided by PyCUDA’s sister project PyOpenCL <https://pypi.org/project/pyopencl>_.