Result for 209E3D7BA5C6C65DBDB893059E97F49C86101E4D

Query result

Key Value
FileName./usr/share/doc/python-pycuda-doc/html/_static/up.png
FileSize345
MD55B3EA372A83281D587C551E8051F61EA
SHA-1209E3D7BA5C6C65DBDB893059E97F49C86101E4D
SHA-256CDBBB0F4925C41710F84C64CC05CAB7587BA52CCDBB37817C0E5BE47111F1511
SSDEEP6:6v/lhPfp8RFkNSg6sp739JO858SBf/HC+/2ZSTQrG6tsup:6v/7uKN6spe8CYHf22QSQ
TLSHT198E0EB86B0209BE9E800E053730219E0E8A1228CC9638204C44D8029A42CA1C48A1A53
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
FileSize121614
MD5A968F7D8653138564224C83B34C9ECD7
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.
PackageMaintainerTomasz Rybak <tomasz.rybak@post.pl>
PackageNamepython-pycuda-doc
PackageSectioncontrib/doc
PackageVersion2016.1.2+git20161024-1
SHA-10DE78B3768687D027F7C1ED5EE07B86972F1311A
SHA-256A2FB82540E0DA62E7B870A07EFEB2BA0B2199878929C33AAE101006DD95C7834