Result for 00F4FAD19553AF107A5D46404910754F4C33128E

Query result

Key Value
FileName./usr/lib64/python3.8/site-packages/numba/cuda/tests/cudapy/__pycache__/test_sync.cpython-38.opt-1.pyc
FileSize6284
MD5C6A25A679AD5599EC847D60E033D0BD5
SHA-100F4FAD19553AF107A5D46404910754F4C33128E
SHA-256A86CA60E5995C9A646A5DA39364291BD95015A306607F63B50C0128C29913726
SSDEEP192:NLK2OtrXuGrZa7yyngmrczJS6+mbzqC3X:E2aDuGrZa7yynzrczJShmbzqgX
TLSHT1D1D100844787AE5BF866F9F8452A03076227D7F5138F4203A918F59B7D8B3E26C64198
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
MD5B764D08CFAEE3AF4221DF7215859120B
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.
PackageNamepython3-numba
PackageRelease3.3
PackageVersion0.48.0
SHA-19EE3AC165B646510E966FBAF13E4F1739D692098
SHA-256D1B9933DC5C1E5226EECEEAE67919520C41E0CB57825907771AF1433D9450C71