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Name: benchdnn | Distribution: openSUSE Leap 15.2 |
Version: 1.4 | Vendor: openSUSE |
Release: lp152.1.2 | Build date: Sat May 16 20:52:19 2020 |
Group: Unspecified | Build host: lamb25 |
Size: 5265342 | Source RPM: mkl-dnn-1.4-lp152.1.2.src.rpm |
Packager: https://bugs.opensuse.org | |
Url: https://01.org/mkl-dnn | |
Summary: Header files of Intel(R) Math Kernel Library |
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 frameworks on Intel architecture. Intel MKL-DNN contains vectorized and threaded building blocks that you can use to implement deep neural networks (DNN) with C and C++ interfaces. This package only includes the benchmark utility including its input files.
Apache-2.0
* Tue May 05 2020 Tomáš Chvátal <tchvatal@suse.com> - Update to 1.4: * Performance improvements all over the board - Rebase patch cmake-no-install-ocl-cmake.patch * Tue Mar 24 2020 Tomáš Chvátal <tchvatal@suse.com> - Add constraints to not crash during testing on OOM * Thu Feb 27 2020 Tomáš Chvátal <tchvatal@suse.com> - Do not disable LTO there is no actual reason for that - Export LD_LIBRARY_PATH to fix older releases build * Wed Feb 26 2020 Tomáš Chvátal <tchvatal@suse.com> - There is no actual reason to not use github tag for tarball fetching -> remove the service - Format with spec-cleaner - Use proper %cmake macros everywhere - Add configure options for cmake to set it up in a way we really want - Add patch from Debian to not install OpenCL cmake finder: * cmake-no-install-ocl-cmake.patch * Thu Feb 20 2020 Christian Goll <cgoll@suse.com> - enabled tests * Thu Jan 30 2020 Christian Goll <cgoll@suse.com> - packaged separate benchnn packae with its input files - updated to v1.1.3 which includes * Fixed the mean and variance memory descriptors in layer normalization (65f1908) * Fixed the layer normalization formula (c176ceb) * Wed Jan 08 2020 Christian Goll <cgoll@suse.com> - updated to v1.1.2 * Fixed threading over the spatial in bfloat16 batched normalization (017b6c9) * Fixed read past end-of-buffer error for int8 convolution (7d6f45e) * Fixed condition for dispatching optimized channel blocking in fp32 backward convolution on Intel Xeon Phi(TM) processor (846eba1) * Fixed fp32 backward convolution for shapes with spatial strides over the depth dimension (002e3ab) * Fixed softmax with zero sizes on GPU (936bff4) * Fixed int8 deconvolution with dilation when ih <= dh (3e3bacb) * Enabled back fp32 -> u8 reorder for RNN (a2c2507) * Fixed segmentation fault in bfloat16 backward convolution from kd_padding=0 computation (52d476c) * Fixed segmentation fault in bfloat16 forward convolution due to push/pop imbalance (4f6e3d5) * Fixed library version for OS X build (0d85005) * Fixed padding by channels in concat (a265c7d) * Added full text of third party licenses and copyright notices to LICENSE file (79f204c) * Added separate README for binary packages (28f4c96) * Fixed computing per-oc mask in RNN (ff3ffab) * Added workaround for number of cores calculation in Xbyak (301b088) * Mon Feb 11 2019 cgoll@suse.com - added ARCH_OPT_FLAGS="" * Tue Feb 05 2019 Christian Goll <cgoll@suse.com> - Initial checking of the Intel(R) Math Kernel Library for Deep Neural Networks which can be used by: * tensorflow * Caffee * PyTorch and other machine learning tools
/usr/bin/benchdnn /usr/share/benchdnn /usr/share/benchdnn/inputs /usr/share/benchdnn/inputs/binary /usr/share/benchdnn/inputs/binary/harness_binary_bf16 /usr/share/benchdnn/inputs/binary/harness_binary_bf16_attr /usr/share/benchdnn/inputs/binary/harness_binary_f32 /usr/share/benchdnn/inputs/binary/harness_binary_f32_attr /usr/share/benchdnn/inputs/binary/harness_binary_i8 /usr/share/benchdnn/inputs/binary/harness_binary_i8_attr /usr/share/benchdnn/inputs/binary/shapes_common /usr/share/benchdnn/inputs/binary/test_binary_all /usr/share/benchdnn/inputs/binary/test_binary_bf16 /usr/share/benchdnn/inputs/binary/test_binary_f32 /usr/share/benchdnn/inputs/binary/test_binary_gpu /usr/share/benchdnn/inputs/binary/test_binary_i8 /usr/share/benchdnn/inputs/bnorm /usr/share/benchdnn/inputs/bnorm/bnorm_1d /usr/share/benchdnn/inputs/bnorm/bnorm_3d /usr/share/benchdnn/inputs/bnorm/bnorm_densenet_121 /usr/share/benchdnn/inputs/bnorm/bnorm_googlenet_v2 /usr/share/benchdnn/inputs/bnorm/bnorm_googlenet_v3 /usr/share/benchdnn/inputs/bnorm/bnorm_large /usr/share/benchdnn/inputs/bnorm/bnorm_regressions /usr/share/benchdnn/inputs/bnorm/bnorm_resnet_50 /usr/share/benchdnn/inputs/bnorm/bnorm_topo /usr/share/benchdnn/inputs/bnorm/bnorm_topo_gpu /usr/share/benchdnn/inputs/bnorm/bnorm_topo_small /usr/share/benchdnn/inputs/bnorm/test_bnorm_all /usr/share/benchdnn/inputs/bnorm/test_bnorm_bfloat16 /usr/share/benchdnn/inputs/bnorm/test_bnorm_gpu /usr/share/benchdnn/inputs/bnorm/test_bnorm_large_gpu /usr/share/benchdnn/inputs/bnorm/test_bnorm_regressions /usr/share/benchdnn/inputs/bnorm/test_bnorm_regressions_large /usr/share/benchdnn/inputs/concat /usr/share/benchdnn/inputs/concat/test_concat_all /usr/share/benchdnn/inputs/concat/test_concat_bfloat16 /usr/share/benchdnn/inputs/concat/test_concat_gpu /usr/share/benchdnn/inputs/conv /usr/share/benchdnn/inputs/conv/harness_conv_attrs_gpu /usr/share/benchdnn/inputs/conv/harness_conv_attrs_int8 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Fabrice Bellet, Tue Jul 9 11:31:35 2024