Result for 64E79701555CB7A2A69567408F86BFF7276DCBE4

Query result

Key Value
FileName./usr/lib/python3/dist-packages/pycuda-2013.1.1.egg-info/top_level.txt
FileSize27
MD5CD8825DB0655E9A29416AF676D27D9A0
SHA-164E79701555CB7A2A69567408F86BFF7276DCBE4
SHA-256E2BF35C8038B2C71ABC4A8E390BA457F54D0E034E8968C048CDF661E954C5672
SSDEEP3:s9IV4Wd:6o4Wd
TLSH
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
FileSize274644
MD5ED8AD29029BB381193C32B3F4033D624
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
PackageVersion2013.1.1+git20140310-1ubuntu1
SHA-1CC44752730EAE48E9322FD9E9E6E8627120EDD40
SHA-256B56B41089750487F10DC079C86C92F32FA0E895C44CB1C93819F29AA2A259147