Result for 2F04A03CAA533F1F2C451752D9D013C1D3A9E1DD

Query result

Key Value
FileName./usr/lib/python3/dist-packages/pycuda/scan.py
FileSize13074
MD5B5B8754771452D6F459EEA175F7265B3
SHA-12F04A03CAA533F1F2C451752D9D013C1D3A9E1DD
SHA-2560A22B62C03F8FD23DEA8E09F8D4F7A7AD24F11BBFC43A47190A87E62EE755A78
SSDEEP384:Z3+54lY7pm555DQh6ZPpsI2TSZNe753A+2:Zrlih6ZPpsTTSZI53A+2
TLSHT11042773A3B1350565A6311B92B8E30013109E54725CEAD947B1D43B01FAB52BF7BEBDD
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
FileSize305544
MD5E4F8E3FB210F874CA5CEA867588F3D49
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.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamepython3-pycuda
PackageSectioncontrib/python
PackageVersion2016.1-1
SHA-1BAD8865EF06912FDC2722B07929A2F227DAC7D10
SHA-2565EA5CC2232706D0BF8DD888AB4C73779A3F800658589177601A4BC8CBDF47F72