Parents (Total: 35)

The searched file hash is included in 35 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
FileSize	290830
MD5	ECD3EFCFE4F75515834707D724A8BE41
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	2014.1-3
SHA-1	04E0D8145D3810F0F28BAB86ADB02E7BFD9A6F8B
SHA-256	FD0D0950BAE13D67BB17FE667E05609102B44E4B21B78C42D964C0DF4956F9D6

Key	Value
FileSize	314760
MD5	7BF0E78717DA26E6338F7BD64DFF900B
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	2018.1.1-3
SHA-1	194237D7D664B504399F5E9BEB56DA0152F10DE1
SHA-256	0FEACB25B38F54A02704784F4224BB36C6531EEC2F66E69507FBA85570A04353

Key	Value
FileSize	466804
MD5	DA783AD042E8BC1B24DD7F017C98259F
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	2018.1.1-4+b1
SHA-1	216F72108B66361221097E375E4CD57619E47A32
SHA-256	7A9AE4FC9D93F552529DD8A9DA555A4C27DAC0151B8581BE57D7E041E349A221

Key	Value
FileSize	315996
MD5	9DAD2B920C5F38AE16418FC825678E96
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	2018.1.1-3+b1
SHA-1	2412174B9D481D80C4933275CF9CBD46E43E7B01
SHA-256	60FF2DA05EC2E8335D75BECDCFC5995F3A6FBAA1B7CE00713F30312582B9A4E4

Key	Value
FileSize	286710
MD5	55C4D78828DD6C5D174DFA73AB3A8362
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	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	python-pycuda
PackageSection	contrib/python
PackageVersion	2013.1.1+git20140310-1ubuntu1
SHA-1	365759025B6938D917128F6A5D9A7120493AA77D
SHA-256	A890F86665FF33AED134EB603581ADD813BAE668D3AB30AEB8A0830AEF2B812E

Key	Value
FileSize	295882
MD5	CDDA0DCCC9DBCA6143B952EEE0B38FBA
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	2014.1-3
SHA-1	3813DE52F4C7FB34A679A751F1AB68D4CE3A8E7A
SHA-256	BB42B676C5A967925887C2604B20645485086CB062B8A2E5060DA030A87CC087

Key	Value
FileSize	315604
MD5	6D7EDAD4A85900295C8656C15C55898B
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	Debian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageName	python-pycuda
PackageSection	contrib/python
PackageVersion	2018.1.1-3
SHA-1	4EF4ADCC6E4220EFE21307798BDCFF45E7722ABD
SHA-256	A84B96264AAFE4114348062D6CA1C01661D2B1C3079FA0EBFD6981F3A0794D5A

Key	Value
MD5	BA6ED9E2057F04E33B7203B93019EE5A
PackageArch	x86_64
PackageDescription	PyCUDA lets you access NVIDIA's CUDA parallel computation API from Python 3.
PackageMaintainer	daviddavid <daviddavid>
PackageName	python3-pycuda
PackageRelease	2.mga7.nonfree
PackageVersion	2018.1.1
SHA-1	514707E56A9270BFCE760C39DEDB47B5DDBBA4A9
SHA-256	FF83A7F9CDD3C11E94B62A7E8657B497D1181F7C4C4201B9BE53E3E2C3122A99

Key	Value
MD5	24A50AC218C6D868AEA50CE43DC85755
PackageArch	x86_64
PackageDescription	PyCUDA lets you access NVIDIA's CUDA parallel computation API from Python 3.
PackageMaintainer	wally <wally>
PackageName	python3-pycuda
PackageRelease	7.mga8.nonfree
PackageVersion	2019.1.2
SHA-1	5AD469F0EB4A115315A610C8B8E461D0D340F3CA
SHA-256	0F5936855090EA7EFFAA70C80127B3BE578896F254929658D3ED970F1E179FBC

Key	Value
FileSize	319636
MD5	12B0F72FA0DD4D4F5917C93FDDB849FD
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	2018.1.1-4build3
SHA-1	5B6B1750FCBE9A57B1F6EB243119376976F12806
SHA-256	825FD1875E492CEDCD406277A8604BECFE0D7918A2B860445F76084BF5F6DD38