Result for 0168F1C002AAB6AD6D0277CE5980C27B363E2D30

Query result

Key Value
FileName./usr/lib64/python3.8/site-packages/numba/cuda/__pycache__/vectorizers.cpython-38.opt-1.pyc
FileSize2762
MD5FFDF15E66B67A8C08AE107D86B89FBC6
SHA-10168F1C002AAB6AD6D0277CE5980C27B363E2D30
SHA-256A5E9367060781990D799A55B9E58B158A1ECF1DEDDF4AE58BD4ECC26636F79E7
SSDEEP48:SYyK9t/wDCI5iEt3D24p9CNLZu7ryILGYsObMSlzgd1s:M6Yn9fpOZuHvHsObO7s
TLSHT1FE5110C495801F9BF956F1FD51EA812512A5C2B3C609A12B8E4C126B7F4B08D4BF544B
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
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