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
FileName	http://archlinux.mirror.root.lu//pool//community//python-pycuda-2020.1-5-x86_64.pkg.tar.zst
MD5	066B2489BB478F95D4F682D742C55620
SHA-1	82BED4CFF8F89EB63CAB66EC201C1443734FDC43
SHA-256	C645148D1025A142BA25054DF385CC9EEAE4E9300340164BAC34D362FE1B67C2
SSDEEP	12288:tGz5AWvkiCzEPJybGXpqhLvF6MxG/NovAAC:tG1VvkiGEPJyqZivF6NMAAC
TLSH	T1198423FA4EE45BB6BA1988B030F24FAC3C3F3058ED14ACA78B21566427977478B13557

Key	Value
FileSize	322664
MD5	8101243AA5186EE90F7211228EFAFA77
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	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	python3-pycuda
PackageSection	contrib/python
PackageVersion	2020.1~dfsg1-1
SHA-1	D952B9E29089EFAFA846DEC00E7082ADD146A4F0
SHA-256	84F08C60CF419A15F89DE1FEDA50FEEB173C4DEE1CDD4C1150231863F0824AEC

Key	Value
FileName	http://archlinux.mirror.root.lu//pool//community//python-pycuda-2020.1-6-x86_64.pkg.tar.zst
MD5	60357CE38064ED1090E149D3D799405F
SHA-1	8755DF05EE71BB9BBB706F12E09F59F92A7CBFEF
SHA-256	9F506406F63A49A378610EC5FDBEE231A355D26A248995B0BB6986E1441CDB5A
SSDEEP	12288:iXx4F958xZrpCksgo3tmBF3Qlca/WtQWxYMf0K:ex4FIvrJfBBF3QCYwQQdz
TLSH	T11A84233F940FC49AE3965D8C1538EBEBC37402E22D97A14172AE559CADC1543CE86DBC

Key	Value
FileSize	290192
MD5	56CCEB4D76564A4B3461A7FF24960D44
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	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	python3-pycuda
PackageSection	contrib/python
PackageVersion	2020.1~dfsg1-1build1
SHA-1	F3C6FBC07D9AC130D1196A11DD4D50F87B88B646
SHA-256	3247C5A79A72E2091CF28CB1719117D5B1E21D628C894289723B71FEBD6990CF

Key	Value
FileSize	324172
MD5	95571647075E84C6DEAE2BF9F998EAE1
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	Debian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageName	python3-pycuda
PackageSection	contrib/python
PackageVersion	2020.1~dfsg1-1
SHA-1	6D7B76D4A0C3460CDB396C06BCAF2B7BE741D316
SHA-256	EE0DD23354FC91FF5644058F857FA3492331625C53180DE417FE4863AABD9556