Result for 157FD63B98851A81B9DB293B10D84DB15EBD3F13

Query result

Key Value
FileName./usr/lib/python3/dist-packages/pycuda/cumath.py
FileSize4835
MD5F9922651AA0D42514B51C99A94BB3A3A
SHA-1157FD63B98851A81B9DB293B10D84DB15EBD3F13
SHA-256A3CDFF2B07D7F1700DA99D276FD8ACA22DC756040E94C1212FE0170E74CBBA81
SSDEEP96:pjmsWwTfp+2WyMY54o9EMPO54Q99lASJx43M4N4cLzu0EGUY43446Rv:pzfsqT54MDG5piSPNXkpdhhv
TLSHT18DA13F6E6E8294ADA6CA354D0CEA18C363C1E137394028E97F4F7A990F3951CB7751AC
hashlookup:parent-total12
hashlookup:trust100

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Parents (Total: 12)

The searched file hash is included in 12 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD5BF00AD6446827EF575A4D82972384731
PackageArchx86_64
PackageDescriptionPyCUDA lets you access NVIDIA's CUDA parallel computation API from Python 3.
PackageMaintainerumeabot <umeabot>
PackageNamepython3-pycuda
PackageRelease6.mga9.nonfree
PackageVersion2021.1
SHA-17A67666264E01AC7EEABEF966798D9E42C828AEB
SHA-25620DC594119ED693ACF48DB2A08DDC146D80F7217825A661ABC44910723FC1DB0
Key Value
FileSize358152
MD5C3825CF1F0AEDC95033009FB7B94B79D
PackageDescriptionPython 3 module to access Nvidia‘s CUDA parallel computation API PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python. Several wrappers of the CUDA API already exist–so what’s so special about PyCUDA? * Object cleanup tied to lifetime of objects. This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code. PyCUDA knows about dependencies, too, so (for example) it won’t detach from a context before all memory allocated in it is also freed. * Convenience. Abstractions like pycuda.driver.SourceModule and pycuda.gpuarray.GPUArray make CUDA programming even more convenient than with Nvidia’s C-based runtime. * Completeness. PyCUDA puts the full power of CUDA’s driver API at your disposal, if you wish. * Automatic Error Checking. All CUDA errors are automatically translated into Python exceptions. * Speed. PyCUDA’s base layer is written in C++, so all the niceties above are virtually free. * Helpful Documentation. . This package contains Python 3 modules.
PackageMaintainerDebian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageNamepython3-pycuda
PackageSectioncontrib/python
PackageVersion2021.1~dfsg-3
SHA-112B61B011445E8184C46C2B00A7EE21E1BCE300D
SHA-256DA26AF59EF98CC358ABE81F9B664005E4685AD4ECBD5F68B1121CF8C92CD3329
Key Value
FileSize326228
MD5C4B7CF72236FF956715CBDEA70CFD2EA
PackageDescriptionPython 3 module to access Nvidia‘s CUDA parallel computation API PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python. Several wrappers of the CUDA API already exist–so what’s so special about PyCUDA? * Object cleanup tied to lifetime of objects. This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code. PyCUDA knows about dependencies, too, so (for example) it won’t detach from a context before all memory allocated in it is also freed. * Convenience. Abstractions like pycuda.driver.SourceModule and pycuda.gpuarray.GPUArray make CUDA programming even more convenient than with Nvidia’s C-based runtime. * Completeness. PyCUDA puts the full power of CUDA’s driver API at your disposal, if you wish. * Automatic Error Checking. All CUDA errors are automatically translated into Python exceptions. * Speed. PyCUDA’s base layer is written in C++, so all the niceties above are virtually free. * Helpful Documentation. . This package contains Python 3 modules.
PackageMaintainerDebian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageNamepython3-pycuda
PackageSectioncontrib/python
PackageVersion2021.1~dfsg-1
SHA-19F7A98EB518E52822AC6136677AC50C67EAFD661
SHA-2569FBB5D28C3D79D68B9E261A036C2FA6FFED80F4681E820562197615C19959FEC
Key Value
FileSize329524
MD5E39E829BAED401402799183298168ED6
PackageDescriptionPython 3 module to access Nvidia‘s CUDA parallel computation API PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python. Several wrappers of the CUDA API already exist–so what’s so special about PyCUDA? * Object cleanup tied to lifetime of objects. This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code. PyCUDA knows about dependencies, too, so (for example) it won’t detach from a context before all memory allocated in it is also freed. * Convenience. Abstractions like pycuda.driver.SourceModule and pycuda.gpuarray.GPUArray make CUDA programming even more convenient than with Nvidia’s C-based runtime. * Completeness. PyCUDA puts the full power of CUDA’s driver API at your disposal, if you wish. * Automatic Error Checking. All CUDA errors are automatically translated into Python exceptions. * Speed. PyCUDA’s base layer is written in C++, so all the niceties above are virtually free. * Helpful Documentation. . This package contains Python 3 modules.
PackageMaintainerDebian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageNamepython3-pycuda
PackageSectioncontrib/python
PackageVersion2022.1~dfsg-1+b2
SHA-1B3D937C10BD617F39313EF47652B115BC308CA8D
SHA-25683CE93FCC2DBBC2794C362EEA69DCDB61E037AA823C4929F0DDB7A77B503615D
Key Value
MD5B17F988A05FE4B655505D609C3AAEA20
PackageArchx86_64
PackageDescriptionPyCUDA lets you access NVIDIA's CUDA parallel computation API from Python 3.
PackageMaintainerpapoteur <papoteur>
PackageNamepython3-pycuda
PackageRelease7.mga9.nonfree
PackageVersion2021.1
SHA-1F0FE1B6C7F95BB14573CBA564CF136472469DF45
SHA-256EB43A2D50E40EC81130D25E93C703020AC5595B8B5A9F1266FDF4BA120275E48
Key Value
FileSize329868
MD5B20771B302CCA183D48CD62C2A0BD199
PackageDescriptionPython 3 module to access Nvidia‘s CUDA parallel computation API PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python. Several wrappers of the CUDA API already exist–so what’s so special about PyCUDA? * Object cleanup tied to lifetime of objects. This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code. PyCUDA knows about dependencies, too, so (for example) it won’t detach from a context before all memory allocated in it is also freed. * Convenience. Abstractions like pycuda.driver.SourceModule and pycuda.gpuarray.GPUArray make CUDA programming even more convenient than with Nvidia’s C-based runtime. * Completeness. PyCUDA puts the full power of CUDA’s driver API at your disposal, if you wish. * Automatic Error Checking. All CUDA errors are automatically translated into Python exceptions. * Speed. PyCUDA’s base layer is written in C++, so all the niceties above are virtually free. * Helpful Documentation. . This package contains Python 3 modules.
PackageMaintainerDebian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageNamepython3-pycuda
PackageSectioncontrib/python
PackageVersion2022.1~dfsg-1+b1
SHA-1E9D4E75818EF362C4825EE91D654B9025D1911BD
SHA-256C5D555ADD3F52F843FF7D322831B2EA5CB28BE79EE931CA8043D859596451DE5
Key Value
FileSize314308
MD5017D08C1CB5EE436FD7377EC13B1F147
PackageDescriptionPython 3 module to access Nvidia‘s CUDA parallel computation API PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python. Several wrappers of the CUDA API already exist–so what’s so special about PyCUDA? * Object cleanup tied to lifetime of objects. This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code. PyCUDA knows about dependencies, too, so (for example) it won’t detach from a context before all memory allocated in it is also freed. * Convenience. Abstractions like pycuda.driver.SourceModule and pycuda.gpuarray.GPUArray make CUDA programming even more convenient than with Nvidia’s C-based runtime. * Completeness. PyCUDA puts the full power of CUDA’s driver API at your disposal, if you wish. * Automatic Error Checking. All CUDA errors are automatically translated into Python exceptions. * Speed. PyCUDA’s base layer is written in C++, so all the niceties above are virtually free. * Helpful Documentation. . This package contains Python 3 modules.
PackageMaintainerDebian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageNamepython3-pycuda
PackageSectioncontrib/python
PackageVersion2022.2.2~dfsg-2+b1
SHA-1F6586DE689141BB88F93E9A6FF9CD3D28765186E
SHA-256D40BBD22F382C1DCA63A03BDC16DDAEDEEAFC1E00C973078C672FEA072AB8650
Key Value
FileSize316184
MD52759CA0B5317934D9C1E387CFADEE0E0
PackageDescriptionPython 3 module to access Nvidia‘s CUDA parallel computation API PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python. Several wrappers of the CUDA API already exist–so what’s so special about PyCUDA? * Object cleanup tied to lifetime of objects. This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code. PyCUDA knows about dependencies, too, so (for example) it won’t detach from a context before all memory allocated in it is also freed. * Convenience. Abstractions like pycuda.driver.SourceModule and pycuda.gpuarray.GPUArray make CUDA programming even more convenient than with Nvidia’s C-based runtime. * Completeness. PyCUDA puts the full power of CUDA’s driver API at your disposal, if you wish. * Automatic Error Checking. All CUDA errors are automatically translated into Python exceptions. * Speed. PyCUDA’s base layer is written in C++, so all the niceties above are virtually free. * Helpful Documentation. . This package contains Python 3 modules.
PackageMaintainerDebian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageNamepython3-pycuda
PackageSectioncontrib/python
PackageVersion2022.1~dfsg-1+b2
SHA-13E6979DE298CF0818067A4899F2B1BBAD69D23CC
SHA-256AF4FD2CB4EFB5B2E9A1ED4352075823F959EE5E22F3FF8140215C3C6E79CE599
Key Value
FileSize357428
MD5ABAA91E2C5CA6A2C4673BDD1715D7865
PackageDescriptionPython 3 module to access Nvidia‘s CUDA parallel computation API PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python. Several wrappers of the CUDA API already exist–so what’s so special about PyCUDA? * Object cleanup tied to lifetime of objects. This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code. PyCUDA knows about dependencies, too, so (for example) it won’t detach from a context before all memory allocated in it is also freed. * Convenience. Abstractions like pycuda.driver.SourceModule and pycuda.gpuarray.GPUArray make CUDA programming even more convenient than with Nvidia’s C-based runtime. * Completeness. PyCUDA puts the full power of CUDA’s driver API at your disposal, if you wish. * Automatic Error Checking. All CUDA errors are automatically translated into Python exceptions. * Speed. PyCUDA’s base layer is written in C++, so all the niceties above are virtually free. * Helpful Documentation. . This package contains Python 3 modules.
PackageMaintainerDebian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageNamepython3-pycuda
PackageSectioncontrib/python
PackageVersion2021.1~dfsg-2
SHA-18938FDA922799F7BA27D764E23DA9996492E9024
SHA-2566D4BE14434A9A8CAF577D0687714D73F62AB0815FE90F2708E8B9F92DA2196E2
Key Value
FileSize365912
MD5F1996CF668237BB6A9C352FEF1D96F4A
PackageDescriptionPython 3 module to access Nvidia‘s CUDA parallel computation API PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python. Several wrappers of the CUDA API already exist–so what’s so special about PyCUDA? * Object cleanup tied to lifetime of objects. This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code. PyCUDA knows about dependencies, too, so (for example) it won’t detach from a context before all memory allocated in it is also freed. * Convenience. Abstractions like pycuda.driver.SourceModule and pycuda.gpuarray.GPUArray make CUDA programming even more convenient than with Nvidia’s C-based runtime. * Completeness. PyCUDA puts the full power of CUDA’s driver API at your disposal, if you wish. * Automatic Error Checking. All CUDA errors are automatically translated into Python exceptions. * Speed. PyCUDA’s base layer is written in C++, so all the niceties above are virtually free. * Helpful Documentation. . This package contains Python 3 modules.
PackageMaintainerDebian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageNamepython3-pycuda
PackageSectioncontrib/python
PackageVersion2021.1~dfsg-2
SHA-15CCBA9F4C5A56AD9B78B3791A34A5F26B5C917BE
SHA-256651F1600425FF7485C70FCC8287486DCE510891F1FE293E71BA326689A6C6352
Key Value
FileSize329116
MD5DBFB5E9DCBB1E29E6F6CBDD9B0F15272
PackageDescriptionPython 3 module to access Nvidia‘s CUDA parallel computation API PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python. Several wrappers of the CUDA API already exist–so what’s so special about PyCUDA? * Object cleanup tied to lifetime of objects. This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code. PyCUDA knows about dependencies, too, so (for example) it won’t detach from a context before all memory allocated in it is also freed. * Convenience. Abstractions like pycuda.driver.SourceModule and pycuda.gpuarray.GPUArray make CUDA programming even more convenient than with Nvidia’s C-based runtime. * Completeness. PyCUDA puts the full power of CUDA’s driver API at your disposal, if you wish. * Automatic Error Checking. All CUDA errors are automatically translated into Python exceptions. * Speed. PyCUDA’s base layer is written in C++, so all the niceties above are virtually free. * Helpful Documentation. . This package contains Python 3 modules.
PackageMaintainerDebian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageNamepython3-pycuda
PackageSectioncontrib/python
PackageVersion2022.2.2~dfsg-2+b1
SHA-1CD0148477C4C6AE7F6219E19AC93E6525E224641
SHA-256DB7F7B242CE5FAF91B542B1D8EDEBDAA213BC4C99334E2B316E1D035F2E92A0B
Key Value
FileSize373996
MD5791C5DDDC48A68DB22B0394F2143A9CF
PackageDescriptionPython 3 module to access Nvidia‘s CUDA parallel computation API PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python. Several wrappers of the CUDA API already exist–so what’s so special about PyCUDA? * Object cleanup tied to lifetime of objects. This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code. PyCUDA knows about dependencies, too, so (for example) it won’t detach from a context before all memory allocated in it is also freed. * Convenience. Abstractions like pycuda.driver.SourceModule and pycuda.gpuarray.GPUArray make CUDA programming even more convenient than with Nvidia’s C-based runtime. * Completeness. PyCUDA puts the full power of CUDA’s driver API at your disposal, if you wish. * Automatic Error Checking. All CUDA errors are automatically translated into Python exceptions. * Speed. PyCUDA’s base layer is written in C++, so all the niceties above are virtually free. * Helpful Documentation. . This package contains Python 3 modules.
PackageMaintainerDebian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageNamepython3-pycuda
PackageSectioncontrib/python
PackageVersion2021.1~dfsg-3
SHA-1EAB297DC56CD1C5BC4A83D603F459BF883EE84C3
SHA-25602335EBB11AE00D2AC4B4A6D02FA0D72CBDC45E7510399D3D3143989AD082983