Result for 12A4FCA4363B6915C7A4D3AAA076282614195592

Query result

Key Value
FileName./usr/share/doc/python3-pycuda/changelog.Debian.arm64.gz
FileSize229
MD5A8517DCA106690EE12D565829029B1BD
SHA-112A4FCA4363B6915C7A4D3AAA076282614195592
SHA-256B9E21C64844B2701EC7A2F9C363C5776D004C017E030072B6DF56B144E4CE6E5
SSDEEP6:XtyefjH5N17rG4I96dO05ZkuiFBVHLCE1V91Htu:XIeLZNtGt9/O4TRu
TLSHT17CD097A97C254E2CEB482A300282F5EB2E4A600A11A8EA1C064C9F10109867399A350A
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
FileSize316184
MD52759CA0B5317934D9C1E387CFADEE0E0
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.
PackageMaintainerDebian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageNamepython3-pycuda
PackageSectioncontrib/python
PackageVersion2022.1~dfsg-1+b2
SHA-13E6979DE298CF0818067A4899F2B1BBAD69D23CC
SHA-256AF4FD2CB4EFB5B2E9A1ED4352075823F959EE5E22F3FF8140215C3C6E79CE599