Parents (Total: 5)

The searched file hash is included in 5 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
FileSize	298216
MD5	32AD609458A16187B987C5C4FDA960CA
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	2016.1.2+git20161024-1+b1
SHA-1	C2BB2E441E22163737447D26772F07AD570E09D0
SHA-256	223AC97F1BA310B9613F8E8D8CD4D99DFDAFA696B72437E46C4FF134903F8486

Key	Value
FileSize	121614
MD5	A968F7D8653138564224C83B34C9ECD7
PackageDescription	module to access Nvidia‘s CUDA computation API (documentation) 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 HTML documentation and example scripts.
PackageMaintainer	Tomasz Rybak <tomasz.rybak@post.pl>
PackageName	python-pycuda-doc
PackageSection	contrib/doc
PackageVersion	2016.1.2+git20161024-1
SHA-1	0DE78B3768687D027F7C1ED5EE07B86972F1311A
SHA-256	A2FB82540E0DA62E7B870A07EFEB2BA0B2199878929C33AAE101006DD95C7834

Key	Value
FileSize	5017462
MD5	B4CE99CF7441FC000118475B2A5FB839
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	2016.1.2+git20161024-1+b1
SHA-1	3034AD8142AC16B9FC320E72610681999230BE81
SHA-256	7D89DB9E077D478C86B6520367F9CBC9D68F41F787C20347DCC0D47FA5D6C1F0

Key	Value
FileSize	4978730
MD5	866D02A6D47766FDAA509B1DF932E70D
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	2016.1.2+git20161024-1+b1
SHA-1	EAA5B38B3B7B86D91ACFAF12A130D058B3216DCC
SHA-256	4A00FAD2ADCE89C3D45D3C30789F2E76F5AE8A76BA07CF0E2904050714A146B6

Key	Value
FileSize	299436
MD5	5C09A9A82BFCC3B00C704260B2081AF5
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	2016.1.2+git20161024-1+b1
SHA-1	86FAC3CD1BF8ECE12209245B7E4D49B63111B989
SHA-256	F4E24A01E77829EDD8C43F958E381751BBA1E684A0CF0BD3FEE305BD53776A77