Result for 00EF9FFC029A727E5F7982F58755B79CF9D0CBEB

Query result

Key Value
FileName./usr/lib64/python3.9/site-packages/numba/cuda/tests/cudadrv/data/__pycache__/__init__.cpython-39.pyc
FileSize167
MD53EAD4627517AFFDEA2817E7A0F4B559D
SHA-100EF9FFC029A727E5F7982F58755B79CF9D0CBEB
SHA-256190F024AD144CD924E0672955465B3CC989ADFB24FD4B3C5D3CE9186993EB0AF
SSDEEP3:wWTavuleh/wZWeplGNMe/VWrzF67yK0SkDp6cRkcTit:LEqeh/wllGebrBfVxD9D6
TLSHT1EEC02B00C112E1D2EC3EBC3C30210B142AD8DD70E38B82C33E08150C1C0D3130C11400
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
MD57E250C677779DB2CE266323F07C030E9
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
PackageNamepython39-numba
PackageRelease1.1
PackageVersion0.54.1
SHA-1653AC357386A704534053478C755717F278F26E1
SHA-25605194971C0F9BB327CC76D4678FD0982630CFED6D52CBB083A2B0AB6514502C5
Key Value
MD54DD5EB932768829652572CC647EF0BF7
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.
PackageNamepython39-numba
PackageRelease54.3
PackageVersion0.55.0
SHA-1FF47E3AC66EF868467ACBFFE1A32A777F506589A
SHA-256592BD62CD26A0AD1B9A3A3F844378A23E9B7784C41C02282A52396D578A69AAB
Key Value
MD59C84078D61357FE6993503AF5D441A31
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.
PackageNamepython39-numba
PackageRelease51.27
PackageVersion0.53.0
SHA-1C8761BA63F3A16CF11A7FD4319E398F7F3124C24
SHA-25679E756AB2DBC796DB97DD8C02CB0684335F6643DB276C771846B35921F0B31F0