Result for 0D16D730CEDBDB639AEEBC7C6402743FAA2CD05C

Query result

Key Value
FileName./usr/lib/python2.7/dist-packages/pycuda/scan.py
FileSize13143
MD5F80DA9337920E7E90672467A98B2A05A
SHA-10D16D730CEDBDB639AEEBC7C6402743FAA2CD05C
SHA-25688C35D7A3C80B20C0A0C115A595073AB1193082810EF471B495E3DE7D0723D8F
SSDEEP384:l3+54lY7pm555DQh6ZPpsI2TSZNe753A+2:lrlih6ZPpsTTSZI53A+2
TLSHT1A642773A3B1350566A6311B92BCE20013109E54726CE6D947B1D43B01FAB52BF7BEBDD
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
FileSize306652
MD50D02A0D38CB8E00C5E9811DD3FDE9D21
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.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamepython-pycuda
PackageSectioncontrib/python
PackageVersion2016.1-1
SHA-1B4A73C073681192B27DE5204D40A99A56791BB5A
SHA-256B1BA37F8A1BCB6CE3367EAE8F4DE5C61046FAB21795F35988D35BAD4A52A403B