Result for 0AFC9438178E27D8CE95707E3B6ED9E50012FAF7

Query result

Key Value
FileName./usr/share/doc/python3-pycuda/changelog.Debian.gz
FileSize1953
MD5B6366B30D38FBAD6DEAEC4DD82C19BEB
SHA-10AFC9438178E27D8CE95707E3B6ED9E50012FAF7
SHA-2563256CAEC581F0E14C64D18B2D3F937834A7A266EB0630040D1BCD3B2C12DFDCC
SSDEEP48:XrxS2/nzBh82AsUf1n16h0k2NjSg2WzDoGm:7xSOzL8o01nUhGjSg2T
TLSHT19D412A84B3A4DF7FF01211148489D19A3A9AB10A085BA61C885E6F19A48FB78D87C510
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
FileSize322664
MD58101243AA5186EE90F7211228EFAFA77
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
PackageVersion2020.1~dfsg1-1
SHA-1D952B9E29089EFAFA846DEC00E7082ADD146A4F0
SHA-25684F08C60CF419A15F89DE1FEDA50FEEB173C4DEE1CDD4C1150231863F0824AEC