Result for 28928EE7A23A3EDBA2359E0393F29C76998462F2

Query result

Key Value
FileName./usr/lib/python3/dist-packages/pycuda/tools.py
FileSize14239
MD5718A4A810863D5A6F94B9D75DE7BDFF4
SHA-128928EE7A23A3EDBA2359E0393F29C76998462F2
SHA-2564DB66EE197B97CDD490E5363CC7957E84DFA7DBD16E8DF7A3F9BFFDACFB9EFC9
SSDEEP384:rcmWF09OgPwrEu4IVStfxUY0HTZVVJfGa+:YlF0pwrEu4KStfxUYA/VJfGa+
TLSHT17C52B55E3852A422A343D92D4CD3F003A36BAE57584C39B0F8ECA1643F45665C2F9FE9
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