Result for 004C93544104F8FB4BB3F17D1ED2516DEA801E02

Query result

Key Value
FileName./usr/lib64/python3.8/site-packages/numba/cuda/tests/cudapy/__pycache__/test_vectorize_device.cpython-38.pyc
FileSize1553
MD5861C66550FA7D5258142FFA3E78FB982
SHA-1004C93544104F8FB4BB3F17D1ED2516DEA801E02
SHA-256E8292E78A433829A9FB10D507B0A6CB6889AF911ECEFF3D52B2814BED0FB3875
SSDEEP24:GlQb6q8AhiYL98PydKR5oGijiyhk8oLtI9TfdPKY79a9ItPFDIPa+:6AZ5gRfwPFopI179cUNDIS+
TLSHT1293127C41D025F53F85BB37B995A110655B1D1E6D30996D3061D9136AF0334E3FC92AD
hashlookup:parent-total3
hashlookup:trust65

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Parents (Total: 3)

The searched file hash is included in 3 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD55A90D3C00EC378B410B78D1FF0464097
PackageArchx86_64
PackageDescriptionNumba is a NumPy-aware optimizing compiler for Python. It uses the LLVM compiler infrastructure to compile Python syntax to machine code. It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls, effectively removing the "interpreter", but not removing the dynamic indirection. Numba is also not a tracing JIT. It *compiles* your code before it gets run, either using run-time type information or type information you provide in the decorator. Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy.
PackageMaintainerhttps://bugs.opensuse.org
PackageNamepython38-numba
PackageRelease1.1
PackageVersion0.54.1
SHA-1A0C3FD5C6CBAB490C4A32015EA2DF316566DCCCC
SHA-256E3B854BB85C41775F7F33961743B07BCC261DA17D1B5EC811A6907A1C0BBDE04
Key Value
MD508EF5CE34C3A1FB1A24EE647B3471425
PackageArchx86_64
PackageDescriptionNumba is a NumPy-aware optimizing compiler for Python. It uses the LLVM compiler infrastructure to compile Python syntax to machine code. It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls, effectively removing the "interpreter", but not removing the dynamic indirection. Numba is also not a tracing JIT. It *compiles* your code before it gets run, either using run-time type information or type information you provide in the decorator. Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy.
PackageNamepython38-numba
PackageRelease54.3
PackageVersion0.55.0
SHA-19DE1E589BA0989870F006F83C3108F7EB870CFAB
SHA-2567D83943CA45A8592B34F38576DC2F6A4E8F9B919EBA71F8661593E9C969B8D6E
Key Value
MD54389388F8F16A2ED893C64CF0A9F9E7B
PackageArchs390x
PackageDescriptionNumba is a NumPy-aware optimizing compiler for Python. It uses the LLVM compiler infrastructure to compile Python syntax to machine code. It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls, effectively removing the "interpreter", but not removing the dynamic indirection. Numba is also not a tracing JIT. It *compiles* your code before it gets run, either using run-time type information or type information you provide in the decorator. Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy.
PackageNamepython38-numba
PackageRelease51.27
PackageVersion0.53.0
SHA-1606EFE2580C6310573F9A60717B59D306C17F467
SHA-25675F8D31F4D17AC99B88215F53A3BDBD39B6C74E650B79ACE1FF5B71DFBF61FE3