Result for BCB9282FA52F0BD3258DCC5D4D9A53A7894D15F8

Query result

Key Value
FileName./usr/share/doc/python3-pycuda/changelog.Debian.gz
FileSize3108
MD5E5AF232EE97E937533EFFEAA71680957
SHA-1BCB9282FA52F0BD3258DCC5D4D9A53A7894D15F8
SHA-25666BF3803B6BC0391FCD17D3ECC404EF5871781E2A70B83EEEC6058110E034867
SSDEEP96:j59PXEpbZxF1EqpHLzHh71eOVIBCU0qwv:zX27zZRzHh71egIbni
TLSHT11C513B56060BD0B5D86B331A456E9001C8D4F891643661B548FA2EB2F6CD74ABCDF38A
hashlookup:parent-total9
hashlookup:trust95

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Parents (Total: 9)

The searched file hash is included in 9 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize6012440
MD50E1F41446CB62EDDE6DCE9DA0E49EA33
PackageDescriptionPython 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.
PackageMaintainerDebian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageNamepython-pycuda-dbg
PackageSectioncontrib/debug
PackageVersion2018.1.1-3+b1
SHA-1BFDE1D38162D0EE603B3ECFA837156793E6DC009
SHA-256589C3E7DC2FB1DCF1B9DFA9D4D6D9C3F576DD36322C1034EEEF13206FBCEBF2B
Key Value
FileSize125184
MD508962F72F9F22A4E82373D2B7C72C32B
PackageDescriptionmodule 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.
PackageMaintainerDebian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageNamepython-pycuda-doc
PackageSectioncontrib/doc
PackageVersion2018.1.1-3
SHA-1AE932020CC541F9AC73BDE13723772C56864CDA2
SHA-2568B7A6937CC1B4A0C3829FF235ACDA7FBDEAE2673A25EAB80B1706D8BAB5877CA
Key Value
FileSize315604
MD56D7EDAD4A85900295C8656C15C55898B
PackageDescriptionPython 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.
PackageMaintainerDebian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageNamepython-pycuda
PackageSectioncontrib/python
PackageVersion2018.1.1-3
SHA-14EF4ADCC6E4220EFE21307798BDCFF45E7722ABD
SHA-256A84B96264AAFE4114348062D6CA1C01661D2B1C3079FA0EBFD6981F3A0794D5A
Key Value
FileSize314760
MD57BF0E78717DA26E6338F7BD64DFF900B
PackageDescriptionPython 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.
PackageMaintainerDebian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageNamepython3-pycuda
PackageSectioncontrib/python
PackageVersion2018.1.1-3
SHA-1194237D7D664B504399F5E9BEB56DA0152F10DE1
SHA-2560FEACB25B38F54A02704784F4224BB36C6531EEC2F66E69507FBA85570A04353
Key Value
FileSize319752
MD5A6DC0B907538D2D9FC2FF6A52BEC8B68
PackageDescriptionPython 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.
PackageMaintainerDebian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageNamepython-pycuda
PackageSectioncontrib/python
PackageVersion2018.1.1-3+b1
SHA-1EF6AC549B53BBA7377B5C6563F704A9990BB757A
SHA-256CB64EEE25F6A011CA5594E24A62553033B1A34992633322D2B06424C4287ABD7
Key Value
FileSize6102308
MD5A29D023E4CD64D91D943B5861F2418AB
PackageDescriptionPython 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.
PackageMaintainerDebian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageNamepython-pycuda-dbg
PackageSectioncontrib/debug
PackageVersion2018.1.1-3
SHA-1FD68414036A1E270CFEE02CFDB3A8C3483267BE9
SHA-2565F8D3DC03C19BC177EBFD496BAC9C54BB7C9AB9FE3F10145FAE1C202BF9582EF
Key Value
FileSize6130904
MD50F1BB1EF134D867F576CD40E10792FA0
PackageDescriptionPython 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.
PackageMaintainerDebian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageNamepython3-pycuda-dbg
PackageSectioncontrib/debug
PackageVersion2018.1.1-3
SHA-1B3562E777A6AEA5E0D60CB901BBEB50CB8716DD1
SHA-256C5302555FC3ECD35F248274D44A3CC6CDD36D771DC2816493E769E8869C53205
Key Value
FileSize6091772
MD5C1E25B67FF11E556069D110A1D3880B3
PackageDescriptionPython 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.
PackageMaintainerDebian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageNamepython3-pycuda-dbg
PackageSectioncontrib/debug
PackageVersion2018.1.1-3+b1
SHA-1A003FA4D55909404560B8015FDB52F00314524F0
SHA-256714DC513BDCAC8FD5BCA1FDE52420B0CF2CD6656FDCF7D49D2A20D2A1BF7915E
Key Value
FileSize315996
MD59DAD2B920C5F38AE16418FC825678E96
PackageDescriptionPython 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.
PackageMaintainerDebian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageNamepython3-pycuda
PackageSectioncontrib/python
PackageVersion2018.1.1-3+b1
SHA-12412174B9D481D80C4933275CF9CBD46E43E7B01
SHA-25660FF2DA05EC2E8335D75BECDCFC5995F3A6FBAA1B7CE00713F30312582B9A4E4