Parents (Total: 4)

The searched file hash is included in 4 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
FileSize	304772
MD5	D605FE9FBDC7B50A73D18FBC4FF81EFE
PackageDescription	Python 3 module to access Nvidia‘s CUDA parallel computation API 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. * 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. . This package contains Python 3 modules.
PackageMaintainer	Tomasz Rybak <tomasz.rybak@post.pl>
PackageName	python3-pycuda
PackageSection	contrib/python
PackageVersion	2017.1.1-1+b1
SHA-1	CF5773E8B7780D41258CA446848E8188A7741D75
SHA-256	880DA4E20469C523AAFE48C8EAE59F600750495381E9961600E2CE11D59716F9

Key	Value
FileSize	5080956
MD5	53899F54CCFDC1CD7CB6F209065532B6
PackageDescription	Python 3 module to access Nvidia‘s CUDA API (debug extensions) 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. * 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. . This package contains debug extensions for the Python 3 debug interpreter.
PackageMaintainer	Tomasz Rybak <tomasz.rybak@post.pl>
PackageName	python3-pycuda-dbg
PackageSection	contrib/debug
PackageVersion	2017.1.1-1+b1
SHA-1	FEA67DAB76DEE4DA622A883497E51238D59D65B5
SHA-256	5233ACF76F7582E35BE8281EB106910372CBDAA1E5CF324D4DD463C6044439D7

Key	Value
FileSize	5048656
MD5	96B1AEAF23F29DB8A29BD17C8D7FF4DF
PackageDescription	Python module to access Nvidia‘s CUDA API (debug extensions) 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. * 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. . This package contains debug extensions build for the Python debug interpreter.
PackageMaintainer	Tomasz Rybak <tomasz.rybak@post.pl>
PackageName	python-pycuda-dbg
PackageSection	contrib/debug
PackageVersion	2017.1.1-1+b1
SHA-1	CBC0E13285FB61FF8F63C1B509E6CBCCECF6D9D4
SHA-256	DB8C00E71F5B52FDD05F82FD7879A7B1FCF4DB3DFE1D88F2CCC6BE4B57B7CA12

Key	Value
FileSize	305108
MD5	90EF2468DFBB2F12EA92F28C818C249F
PackageDescription	Python module to access Nvidia‘s CUDA parallel computation API 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. * 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.
PackageMaintainer	Tomasz Rybak <tomasz.rybak@post.pl>
PackageName	python-pycuda
PackageSection	contrib/python
PackageVersion	2017.1.1-1+b1
SHA-1	D1C5EF7E6BE2DF1137021D5B094F743F9D2610C7
SHA-256	405B8677A1E57615140991C45A66E131D865E1E57B479CB12B3D6F970E7DF391