Result for 05173C408F64FDB83D8DF0FE6327F08A64E7A232

Query result

Key Value
FileName./usr/share/doc/python-pycuda-doc/html/driver.html
FileSize224104
MD54E753F0BCCB257572EE88683D37343AD
SHA-105173C408F64FDB83D8DF0FE6327F08A64E7A232
SHA-256D94C57FFD6AE1982F59D951ADA8BB1F12939A35A8AA896CF4A50513D94E8DDDD
SSDEEP1536:UPtmryTshFQby4f/p6Ue5GSlwjoNUjamFx:UlshFQby4f/cUemjo6jaUx
TLSHT17024312191E10D33D93397E8C79A0B3273E7451AF1A10CD559F9939FC39ACE8F91A81A
hashlookup:parent-total1
hashlookup:trust55

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Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize120940
MD5C11A2D31FECBE0868A8E71B46809B508
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
PackageVersion2016.1-1
SHA-18BAFFD3C51E55146A1D420A04E2F4DEA57C0863C
SHA-256BFC099C7B4E1437D4D4D83206F67E339EB8BB75C1A645039158F17B8C2772062