Result for 1D49B13184552DAB74214748B533ACCC4E2DABFF

Query result

Key Value
FileName./usr/lib/python2.7/dist-packages/pycuda/_pvt_struct.so
FileSize36096
MD548E3D1F2497748E55BF817AD623ADCFD
SHA-11D49B13184552DAB74214748B533ACCC4E2DABFF
SHA-256A9587140E13FF317D92041FBF557C07C995252C19F2816863505887CFEE32D42
SSDEEP384:uJ0lJAk0VFN1vIcaoqAuCzJo5nkN0L9JaD+2VVTC48/d:Y0OVxvInoten00aX6/d
TLSHT1E7F2184BF7E295FEC06593B088838BB1AE34B490836156B36548E7781A51F250F7FE78
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
FileSize304682
MD5CE0BA381CB2F51A9F332F8D8E8767F8C
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.
PackageMaintainerTomasz Rybak <tomasz.rybak@post.pl>
PackageNamepython-pycuda
PackageSectioncontrib/python
PackageVersion2014.1-3
SHA-18951AABF3818022D4EC45C4A1662A43B5ED755EE
SHA-256CA16EB9ADB14EA9379C1705FD381C3E9388031223C58F40F54B8AF67F5678330