Result for 15AD7269B2AC61BD6F24CAC0DB0DF4610DBA2E0B

Query result

Key Value
FileName./usr/share/doc/python-pycuda-doc/changelog.Debian.gz
FileSize1677
MD597F3823A6E1EFFA96695A4D23A2733B2
SHA-115AD7269B2AC61BD6F24CAC0DB0DF4610DBA2E0B
SHA-2563750D13121F9BA24B9CD9AE96406193E834C7ED2B9EB94C35039616A1FF92692
SSDEEP48:XeOT5+hK4UMcUyUzOa9niAZrPFGof2o+W:uw5+vx2s9i6sofBf
TLSHT11A313C53E3DB1AEFE008A4FD0D4D65B1F14CABBA321CD347C8784565115C01EF28D128
hashlookup:parent-total1
hashlookup:trust55

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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
FileSize119808
MD58610CB0C9A5A484B4BEA977BDB93242A
PackageDescriptionmodule to access Nvidia‘s CUDA computation API (documentation) 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 HTML documentation and example scripts.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamepython-pycuda-doc
PackageSectioncontrib/doc
PackageVersion2018.1.1-4build2
SHA-180020ED7FA39EC61C4C8E536392338201BA9CDBA
SHA-25666EBCDE4AB472A2C51AC159F4152B0E1243BB7C87A409F868AC6EB5414590EA8