Result for 00F2E6C62B71C490BF44E0E7E23D9232D9740C93

Query result

Key Value
FileName./usr/share/doc/libmkldnn-dev/html/search/files_4.js
FileSize153
MD547FBCF37993FD66609B5F3254BCA92A1
SHA-100F2E6C62B71C490BF44E0E7E23D9232D9740C93
SHA-256BDBE9FAAD123AF4CEE754FAE4C2A3FD731E637514348B3584BED5F8A12223E25
SSDEEP3:qh+/RQeH2T1M2Hqd7fMuO9KT1M2jtKXcOL8ltQXi1M2wdlJbvBwnyj:qQRQeWTGwqd7fNNTGsMcOQliyGplNvM0
TLSHT1D1C02B14C02EEC2AC3E03137B0D31F4F4F1810123D148CC2785C00158E00A8EBD38309
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
FileSize2241796
MD53CD007F27133590B561162C4EC2C6EBD
PackageDescriptionMath Kernel Library for Deep Neural Networks (doc) Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an open source performance library for deep learning applications. The library accelerates deep learning applications and framework on Intel(R) architecture. Intel(R) MKL-DNN contains vectorized and threaded building blocks which you can use to implement deep neural networks (DNN) with C and C++ interfaces. . DNN functionality optimized for Intel architecture is also included in Intel(R) Math Kernel Library (Intel(R) MKL). API in this implementation is not compatible with Intel MKL-DNN and does not include certain new and experimental features. . One can choose to build Intel MKL-DNN without binary dependency. The resulting version will be fully functional, however performance of certain convolution shapes and sizes and inner product relying on SGEMM function may be suboptimal. . This package contains the doxygen documentation.
PackageMaintainerDebian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
PackageNamelibmkldnn-doc
PackageSectiondoc
PackageVersion0.17.4-1
SHA-1A961EF4279631692A09386A272E4CD80F692E2BB
SHA-256D2432E59A25F42FFED0EEB90354A59DEFA5CD1CDCBDEF610DE2D4ED5CF5491C4