Result for 1BC6FC8FE63C1FD73A84F485E1F46AB21895BFA0

Query result

Key Value
FileName./usr/share/doc/python-pycuda/changelog.Debian.gz
FileSize1341
MD5BB1056290C129683E143C2B0294EBDEB
SHA-11BC6FC8FE63C1FD73A84F485E1F46AB21895BFA0
SHA-2564B840941007984147DE50D74E8F4E0330C1CDD9A5F610221C17D502394536253
SSDEEP24:XcSChxPlC4L1vGceyml6PI0OVV0fhXYJcf/OuU0nhF13o3TL5Jrg1oYC6c+jUyqN:XcVNtptaR0o0hK+/dnlqL3zY+yqOyn
TLSHT15321B6E2E693215365CC57AD4826F9BA0B0FA49504D07D69C2D04B944050AD349D7B38
hashlookup:parent-total1
hashlookup:trust55

Network graph view

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
FileSize306652
MD50D02A0D38CB8E00C5E9811DD3FDE9D21
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.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamepython-pycuda
PackageSectioncontrib/python
PackageVersion2016.1-1
SHA-1B4A73C073681192B27DE5204D40A99A56791BB5A
SHA-256B1BA37F8A1BCB6CE3367EAE8F4DE5C61046FAB21795F35988D35BAD4A52A403B