Result for 0319AA74B323380B6558DAA3E0050C5B06CA9523

Query result

Key Value
FileName./usr/lib/python2.7/dist-packages/pycuda/_driver.so
FileSize1288972
MD5BCB0FEC7873687D12CA3568D6BF556A9
SHA-10319AA74B323380B6558DAA3E0050C5B06CA9523
SHA-2566D1497F1B51737990C4DFB88420366BCF76932CE281641047D0D62FA08A92AFE
SSDEEP24576:4phBvuXH24IiMPfvnBRp6pxybrE82fhP8lHLLug1UMG7Y33Mg7aFokEY54IESIgx:4phBvuXH24IiMPfvnBRp6pxybrE82fhX
TLSHT18E55091FB804F8E3CE5730B9010F9BFA9AA1BBD5565135E2A2C9CA1BDC5374B5E24342
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
FileSize270836
MD5824839BAE101E1F595D7BBFEC4DC0289
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
PackageVersion2013.1.1+git20140310-1ubuntu1
SHA-1ED2B2716DDD78BA7CCBF03C81D3A325B9332C344
SHA-2560FFF8E40AFA02B86ADDE6FE1297EA194CC644688EB27E4C9B282F40A3B84462C