Result for 0C6B1B9B6135EE94ECB6B78C8A61960DB4CE5AE0

Query result

Key Value
FileName./usr/lib/python3/dist-packages/pycuda/gl/autoinit.py
FileSize292
MD570111614C0C27E72482E61F40E33ED41
SHA-10C6B1B9B6135EE94ECB6B78C8A61960DB4CE5AE0
SHA-256F99CD24FE4968F1E83BC1D7101D36704894E62BC2565B77B6D964AB104089203
SSDEEP6:zGaHqOGbOhACTB7XIgYuQfHH9e9/MH9U43D6JqSn2Qin0N0V:zZe4B7XIQQPHvHCo6JjY0w
TLSHT1FCE0C23F6023E9267B22781D5685500E13F2BE1AEB08D8F112AB005E47C6233D0CC324
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