swMATH ID: 9005
Software Authors: NVIDIA
Description: PyCUDA gives you easy, Pythonic access to Nvidia‘s CUDA parallel computation API. Several wrappers of the CUDA API already exist–so why the need for 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.compiler.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. 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. You’re looking at it. ;)
Homepage: http://documen.tician.de/pycuda/
Dependencies: CUDA
Related Software: PyOpenCL; CUDA; Python; SciPy; OpenCL; NumPy; mpi4py; ParaView; Mako; Ncorr; Gmsh; meshio; LAMMPS; Peridigm; PeriPy; PyGBe; CUBLAS; Cython; PyFR; PySPOD
Referenced in: 17 Publications

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