Result for 4E34F97210CF6FDD55FDE6ED75013EE4C09C8F7D

Query result

Key Value
FileName./usr/lib/python3/dist-packages/pycuda/sparse/coordinate.py
FileSize8753
MD518D229C1138C7DEF958D766245659EEC
SHA-14E34F97210CF6FDD55FDE6ED75013EE4C09C8F7D
SHA-25646FD0A4A86F14B6FD05BAF7DE3C8A2D75B2A4D3D7109BF421220E846BA17DDBD
SSDEEP192:LFC7xJCzywiIC54hskWz5rNCNcWl3/Bq30bMxrw:y4ceWz581k30bMxrw
TLSHT1E302708C8835A1D57983481942C7350211A72307198CB6E4FE7C92B05FDEA1AB3AEFED
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
FileSize306638
MD520BDBAB60A23CABB6F145E5DAE81139A
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.
PackageMaintainerTomasz Rybak <tomasz.rybak@post.pl>
PackageNamepython3-pycuda
PackageSectioncontrib/python
PackageVersion2014.1-3
SHA-1711C5ED5C3AEBDB32C077E013C40061F29A82AA9
SHA-256705403069432FD30956221AAC74170A1853DE5B14F3B4B2C7FECB6D4674E3FB1
Key Value
FileSize295882
MD5CDDA0DCCC9DBCA6143B952EEE0B38FBA
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.
PackageMaintainerTomasz Rybak <tomasz.rybak@post.pl>
PackageNamepython3-pycuda
PackageSectioncontrib/python
PackageVersion2014.1-3
SHA-13813DE52F4C7FB34A679A751F1AB68D4CE3A8E7A
SHA-256BB42B676C5A967925887C2604B20645485086CB062B8A2E5060DA030A87CC087