PyCUDA: Pythonic Access to CUDA, with Arrays and Algorithms
PyCUDA lets you access Nvidia's 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 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.
Relatedly, like-minded computing goodness for OpenCL is provided by PyCUDA's sister project PyOpenCL.