Result for 0E0DFDDCF5E21D06A5879120329CE0DC7A82E172

Query result

Key Value
FileName./usr/share/doc/python-pycuda-doc/html/searchindex.js
FileSize47500
MD5B16D6FC2CFB90E05BC688E8C3339C993
SHA-10E0DFDDCF5E21D06A5879120329CE0DC7A82E172
SHA-2560C41A8E94B6CC602AE81FC8FE7EB60CD90DCB95F75757C8F14CE8A9BEE48C8E3
SSDEEP768:DhUAYoVR+rg696yRCM2GtSoEX3B02Z82hQYUcPGbKWiJyEHle8qFWNi:FUAl696yRCM2foEX3u2Z82uYUcwKvyEC
TLSHT1BB2353238868485B1831415FFD8253570E5D62017D5CEE83AEB88DBB61877DAB33BE27
hashlookup:parent-total2
hashlookup:trust60

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Parents (Total: 2)

The searched file hash is included in 2 parent files which include package known and seen by metalookup. A sample is included below:

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
FileSize119808
MD58610CB0C9A5A484B4BEA977BDB93242A
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.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamepython-pycuda-doc
PackageSectioncontrib/doc
PackageVersion2018.1.1-4build2
SHA-180020ED7FA39EC61C4C8E536392338201BA9CDBA
SHA-25666EBCDE4AB472A2C51AC159F4152B0E1243BB7C87A409F868AC6EB5414590EA8