Result for 1D753B3484BCE0388CBEB7B2D56612B4B1BE8F75

Query result

Key Value
FileName./usr/lib/python2.7/dist-packages/pycuda/sparse/cg.py
FileSize7451
MD5E64F0E56D5CED6617675588E22F94A44
SHA-11D753B3484BCE0388CBEB7B2D56612B4B1BE8F75
SHA-2566536508674C44DCA1DD0A6AE43BE777C20E16400ED21D4962F035673747FB58A
SSDEEP192:eHEatt8tRomudD1l769eGFDPFVdNpr9g0pQpnF5sPix29Ou:ekOt8tRjOD1d69eUB9IFZx4
TLSHT10CF145A18DA74C256717EA5BA4C6B403261C2B0BAF0D18B176FCE5547F05320E3A6FF5
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
FileSize290830
MD5ECD3EFCFE4F75515834707D724A8BE41
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.
PackageMaintainerTomasz Rybak <tomasz.rybak@post.pl>
PackageNamepython-pycuda
PackageSectioncontrib/python
PackageVersion2014.1-3
SHA-104E0D8145D3810F0F28BAB86ADB02E7BFD9A6F8B
SHA-256FD0D0950BAE13D67BB17FE667E05609102B44E4B21B78C42D964C0DF4956F9D6
Key Value
FileSize304682
MD5CE0BA381CB2F51A9F332F8D8E8767F8C
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.
PackageMaintainerTomasz Rybak <tomasz.rybak@post.pl>
PackageNamepython-pycuda
PackageSectioncontrib/python
PackageVersion2014.1-3
SHA-18951AABF3818022D4EC45C4A1662A43B5ED755EE
SHA-256CA16EB9ADB14EA9379C1705FD381C3E9388031223C58F40F54B8AF67F5678330