Result for 0150A4B0AE104F8C4A2DD9C592DAC8E0E18E73D3

Query result

Key Value
FileName./usr/lib64/python3.8/site-packages/numba/cuda/tests/cudadrv/test_managed_alloc.py
FileSize4750
MD5B774D8E9A648FC404105A297B5725D20
SHA-10150A4B0AE104F8C4A2DD9C592DAC8E0E18E73D3
SHA-25628FBAFB60377EE1CABA081EB801D4EE7140DF5B49FC28088096BE00DF71431E2
SSDEEP96:X3l+OKW0Z8hmwT5fBw1MK6BV/BV1I0qIkBXkLPXCM/ljBVFU:XaeawqvKPX9/XU
TLSHT147A18531722F023866D752A7B507E6862327C16B86DD1A2C70FEC4385F125FC52D6BE9
hashlookup:parent-total26
hashlookup:trust100

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Parents (Total: 26)

The searched file hash is included in 26 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize1514992
MD5B1285EA3CDBBF12F55FFB3096B8AD0D4
PackageDescriptionnative machine code compiler for Python 3 Numba compiles native machine code instructions from Python programs at runtime using the LLVM compiler infrastructure. It could be easily employed by decorating individual computation intensive functions in the Python code. Numba could significantly speed up the performance of computations, and optionally supports compilation to run on GPU processors through Nvidia's CUDA platform. It integrates well with the Python scientific software stack, and especially recognizes Numpy arrays. . This package contains the modules for Python 3.
PackageMaintainerDebian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
PackageNamepython3-numba
PackageSectionpython
PackageVersion0.52.0-4
SHA-108F898DE47538FFC7B8D77EAA605E24A9C9D6A20
SHA-256E8E4700868F11496000B64F613716A43D0FAEF07C6C2F6845CA0DD4AD4E69722
Key Value
FileSize1518892
MD500BB154353C3A44456E7C37357DC0CF6
PackageDescriptionnative machine code compiler for Python 3 Numba compiles native machine code instructions from Python programs at runtime using the LLVM compiler infrastructure. It could be easily employed by decorating individual computation intensive functions in the Python code. Numba could significantly speed up the performance of computations, and optionally supports compilation to run on GPU processors through Nvidia's CUDA platform. It integrates well with the Python scientific software stack, and especially recognizes Numpy arrays. . This package contains the modules for Python 3.
PackageMaintainerDebian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
PackageNamepython3-numba
PackageSectionpython
PackageVersion0.52.0-4
SHA-10E1B56774FA5E83D6319316258C88D07091A65F4
SHA-2562896127B26DB22BD361AB9A88E8F1C06E83D0CD3701EA75872DC17EA196F684D
Key Value
MD5DCBFC246B43931DC15CE79952EBB4407
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
PackageRelease51.3
PackageVersion0.53.0
SHA-117B952067229CC3FCD88CAD669A603E60B4395B5
SHA-2565A264DDF0E77D82DBFAF36A40B2AA9C28F93C0E130A8317FDBD533F827F8A0D0
Key Value
MD5325F3490BB5CECD5333EC821EDADAF91
PackageArcharmv7hl
PackageDescriptionNumba is a NumPy-aware optimizing compiler for Python. It uses the LLVM compiler infrastructure to compile Python syntax to machine code.
PackageMaintainertmb <tmb>
PackageNamepython3-numba
PackageRelease1.mga8
PackageVersion0.52.0
SHA-117BB7FA2B3BA68DB92A6B0F8E95C2E4D76D56817
SHA-256DE90D3E08A98050FBA23D4A59D3896495C43C1A533184F5E0F932ADA3461960B
Key Value
FileSize1502420
MD5506A38845B05B88B72C41FD3FE3DF053
PackageDescriptionnative machine code compiler for Python 3 Numba compiles native machine code instructions from Python programs at runtime using the LLVM compiler infrastructure. It could be easily employed by decorating individual computation intensive functions in the Python code. Numba could significantly speed up the performance of computations, and optionally supports compilation to run on GPU processors through Nvidia's CUDA platform. It integrates well with the Python scientific software stack, and especially recognizes Numpy arrays. . This package contains the modules for Python 3.
PackageMaintainerDebian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
PackageNamepython3-numba
PackageSectionpython
PackageVersion0.52.0-4
SHA-1181B729A4536B0B3000AD410D60110E415C4067F
SHA-256E2BFBFC0BBC939A6EC3CAD13FB918F291CA16596436F70D8E88AB654186E076C
Key Value
MD53B34138441C4DC8B67C553D0BB6C15F3
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
PackageReleaselp153.51.2
PackageVersion0.53.0
SHA-11AF6D5089D4B71988629C4F977698EA631443119
SHA-256C0DECFA83E87374DCBB381B5171E68F6759B8FEC440A7EC57DB81703EA3E12FF
Key Value
MD5D70EA79722238EADFB872F44B6A1E9C0
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
PackageReleaselp153.2.5
PackageVersion0.53.0
SHA-13253B014B47FF0689926E07A511304DAB46336AD
SHA-256747FF92B10647E7F5786C24596CA96587EEA8E13055C27E5748A8C8ABCA29107
Key Value
MD5AF765F3F63BFB27058677EE25F9B9E8B
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
PackageReleaselp152.2.4
PackageVersion0.53.0
SHA-149547D6D40D6BD13A779F81C9519BEA468CA201D
SHA-256475F9880AA26015E5C571D947A2064B0D821A85D98F10C1A665ED924559569AF
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
Key Value
FileSize1514356
MD5651BB7FF573488A7B5B60756105A0041
PackageDescriptionnative machine code compiler for Python 3 Numba compiles native machine code instructions from Python programs at runtime using the LLVM compiler infrastructure. It could be easily employed by decorating individual computation intensive functions in the Python code. Numba could significantly speed up the performance of computations, and optionally supports compilation to run on GPU processors through Nvidia's CUDA platform. It integrates well with the Python scientific software stack, and especially recognizes Numpy arrays. . This package contains the modules for Python 3.
PackageMaintainerDebian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
PackageNamepython3-numba
PackageSectionpython
PackageVersion0.52.0-4
SHA-1711F8A7129731E3505DCA87F817115A59F4DDEF8
SHA-2565DAFFA46813AC405012BB71C204B22EE96BCEE61BD335551211D2F2C37E19DB4