# mkl-dnn **Repository Path**: nidao/mkl-dnn ## Basic Information - **Project Name**: mkl-dnn - **Description**: Deep Neural Network Library (DNNL) - **Primary Language**: C++ - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2019-08-24 - **Last Updated**: 2020-12-18 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README Deep Neural Network Library (DNNL) ======================================================================== > **Note** > > Starting with version 1.1 the library is renamed to DNNL. Please read > [Intel MKL-DNN to DNNL Transition Guide](https://intel.github.io/mkl-dnn/dev_guide_transition_to_dnnl.html). > **Note** > > Version 1.0 brings incompatible changes to the 0.20 version. Please read > [Version 1.0 Transition Guide](https://intel.github.io/mkl-dnn/dev_guide_transition_to_v1.html). Deep Neural Network Library (DNNL) is an open-source performance library for deep learning applications. The library includes basic building blocks for neural networks optimized for Intel Architecture Processors and Intel Processor Graphics. DNNL is intended for deep learning applications and framework developers interested in improving application performance on Intel CPUs and GPUs. Deep learning practitioners should use one of the applications enabled with DNNL: * [Apache\* MXNet](https://mxnet.apache.org) * [BigDL](https://github.com/intel-analytics/BigDL) * [Caffe\* Optimized for Intel Architecture](https://github.com/intel/caffe) * [Chainer\*](https://chainer.org) * [DeepLearning4J\*](https://deeplearning4j.org) * [Intel Nervana Graph](https://github.com/NervanaSystems/ngraph) * [MATLAB\* Deep Learning Toolbox](https://www.mathworks.com/help/deeplearning/) * [Menoh\*](https://github.com/pfnet-research/menoh) * [Microsoft\* Cognitive Toolkit (CNTK)](https://docs.microsoft.com/en-us/cognitive-toolkit) * [ONNX Runtime](https://github.com/microsoft/onnxruntime) * [OpenVINO(TM) toolkit](https://01.org/openvinotoolkit) * [PaddlePaddle\*](http://www.paddlepaddle.org) * [PyTorch\*](https://pytorch.org/) * [Tensorflow\*](https://www.tensorflow.org) # Installation Pre-built binaries for Linux\*, Windows\*, and macOS\* are available for download in the [releases section](https://github.com/intel/mkl-dnn/releases). Package names use the following convention: | OS | Package name | :------ | :----------- | Linux | `dnnl_lnx__cpu_[_gpu_].tgz` | Windows | `dnnl_win__cpu_[_gpu_].zip` | macOS | `dnnl_mac__cpu_.tgz` Several packages are available for each operating system to ensure interoperability with CPU or GPU runtime libraries used by the application. | Configuration | Dependency | :-------------| :--------- | `cpu_iomp` | Intel OpenMP runtime | `cpu_gomp` | GNU\* OpenMP runtime | `cpu_vcomp` | Microsoft Visual C OpenMP runtime | `cpu_tbb` | Threading Building Blocks The packages do not include library dependencies and these need to be resolved in the application at build time. See the [System Requirements](#system-requirements) section below and the [Build Options](http://intel.github.io/mkl-dnn/dev_guide_build_options.html) section in the [developer guide](http://intel.github.io/mkl-dnn) for more details on CPU and GPU runtimes. If the configuration you need is not available, you can [build the library from source](http://intel.github.io/mkl-dnn/dev_guide_build.html). # Contributing We welcome community contributions to DNNL. If you have an idea on how to improve the library: * For changes impacting the public API, submit an [RFC pull request](CONTRIBUTING.md#RFC_pull_requests). * Ensure that the changes are consistent with the [code contribution guidelines](CONTRIBUTING.md#code_contribution_guidelines) and [coding style](CONTRIBUTING.md#coding_style). * Ensure that you can build the product and run all the examples with your patch. * Submit a [pull request](https://github.com/intel/mkl-dnn/pulls). For additional details, see [contribution guidelines](CONTRIBUTING.md). # Support Please submit your questions, feature requests, and bug reports on the [GitHub issues](https://github.com/intel/mkl-dnn/issues) page. # License DNNL is licensed under [Apache License Version 2.0](http://www.apache.org/licenses/LICENSE-2.0). This software includes the following third-party components: * [Xbyak](https://github.com/herumi/xbyak) distributed under [3-clause BSD licence](src/cpu/xbyak/COPYRIGHT) * [gtest](https://github.com/google/googletest) distributed under [3-clause BSD license](tests/gtests/gtest/LICENSE) * [ittnotify](https://github.com/intel/IntelSEAPI) distributed under [3-clause BSD license](src/cpu/jit_utils/jitprofiling/LICENSE.BSD) * [Cmake](https://github.com/Kitware/CMake) distributed under [3-clause BSD license](cmake/Copyright.txt) * [MathJax](https://github.com/mathjax/MathJax) distributed under [Apache License Version 2.0](doc/assets/mathjax/LICENSE) # Documentation * [Developer guide](https://intel.github.io/mkl-dnn) explains programming model, supported functionality, details of primitives implementations and includes annotated examples. * [API reference](https://intel.github.io/mkl-dnn/modules.html) provides comprehensive reference of the library API. # System Requirements DNNL supports systems based on Intel 64 architecture or compatible processors. The library is optimized for the following CPUs: * Intel Atom processor with Intel SSE4.1 support * 4th, 5th, 6th, 7th, and 8th generation Intel Core(TM) processor * Intel Xeon(R) processor E3, E5, and E7 family (formerly Sandy Bridge, Ivy Bridge, Haswell, and Broadwell) * Intel Xeon Phi(TM) processor (formerly Knights Landing and Knights Mill) * Intel Xeon Scalable processor (formerly Skylake and Cascade Lake) * future Intel Xeon Scalable processor (code name Cooper Lake) DNNL detects instruction set architecture (ISA) in the runtime and uses just-in-time (JIT) code generation to deploy the code optimized for the latest supported ISA. The library is optimized for the following GPUs: * Intel HD Graphics * Intel UHD Graphics * Intel Iris Plus Graphics ## Requirements for Building from Source DNNL supports systems meeting the following requirements: * Operating sytem with Intel 64 architecture support * C++ compiler with C++11 standard support * [CMake](https://cmake.org/download/) 2.8.11 or later * [Doxygen](http://www.doxygen.nl/download.html#srcbin) 1.8.5 or later to build documentation Configurations of CPU and GPU engines may introduce additional build time dependencies. ### CPU Engine Intel Architecture Processors and compatible devices are supported by the DNNL CPU engine. The CPU engine is built by default and cannot be disabled at build time. The engine can be configured to use the OpenMP or TBB threading runtime. The following additional requirements apply: * OpenMP runtime requires C++ compiler with OpenMP 2.0 or later standard support * TBB runtime requires [Threading Building Blocks (TBB)](https://www.threadingbuildingblocks.org/) 2017 or later. Some implementations rely on OpenMP 4.0 SIMD extensions, and we recommend using the Intel C++ Compiler for the best performance results. ### GPU Engine Intel Processor Graphics is supported by the DNNL GPU engine. The GPU engine is disabled in the default build configuration. The following additional requirements apply when GPU engine is enabled: * OpenCL\* runtime library (OpenCL version 1.2 or later) * OpenCL driver (with kernel language support for OpenCL C 2.0 or later) with Intel subgroups extension support ### Runtime Dependencies When DNNL is built from source, the library runtime dependencies and specific versions are defined by the build environment. #### Linux Common dependencies: * System C/C++ runtime (libc.so, libstdc++.so) * Dynamic Linking Library (libdl.so) * C Math Library (libm.so) * POSIX Threads Library (libpthread.so) Runtime specific dependencies: | Runtime configuration | Compiler | Dependency | :----------------------- | :---------------------------- | :--------- | `DNNL_CPU_RUNTIME=OMP` | GCC | GNU OpenMP runtime (libgomp.so) | `DNNL_CPU_RUNTIME=OMP` | Intel C/C++ Compiler | Intel OpenMP runtime (libiomp5.so) | `DNNL_CPU_RUNTIME=OMP` | Clang | Intel OpenMP runtime (libiomp5.so) | `DNNL_CPU_RUNTIME=TBB` | any | Threading Building Blocks (libtbb.so) | `DNNL_GPU_RUNTIME=OCL` | any | Intel Graphics Compute Runtime for OpenCL (libOpenCL.so) #### Windows Common dependencies: * Microsoft Visual C++ Redistributable (msvcrt.dll) Runtime specific dependencies: | Runtime configuration | Compiler | Dependency | :----------------------- | :---------------------------- | :--------- | `DNNL_CPU_RUNTIME=OMP` | Microsoft Visual C++ Compiler | No additional requirements | `DNNL_CPU_RUNTIME=OMP` | Intel C/C++ Compiler | Intel OpenMP runtime (iomp5.dll) | `DNNL_CPU_RUNTIME=TBB` | any | Threading Building Blocks (tbb.dll) | `DNNL_GPU_RUNTIME=OCL` | any | Intel Graphics Driver (OpenCL.dll) #### macOS Common dependencies: * System C/C++ runtime (libc++.dylib, libSystem.dylib) Runtime specific dependencies: | Runtime configuration | Compiler | Dependency | :----------------------- | :---------------------------- | :--------- | `DNNL_CPU_RUNTIME=OMP` | Intel C/C++ Compiler | Intel OpenMP runtime (libiomp5.dylib) | `DNNL_CPU_RUNTIME=TBB` | any | Threading Building Blocks (libtbb.dylib) ### Validated Configurations CPU engine was validated on RedHat\* Enterprise Linux 7 with * GNU Compiler Collection 4.8, 5.4, 6.1, 7.2, and 8.1 * Clang\* 3.8.0 * [Intel C/C++ Compiler](https://software.intel.com/en-us/intel-parallel-studio-xe) 17.0, 18.0, and 19.0 on Windows Server\* 2012 R2 with * Microsoft Visual C++ 14.0 (Visual Studio 2015 Update 3) * [Intel C/C++ Compiler](https://software.intel.com/en-us/intel-parallel-studio-xe) 17.0 and 19.0 on macOS 10.13 (High Sierra) with * Apple LLVM version 9.2 (XCode 9.2) * [Intel C/C++ Compiler](https://software.intel.com/en-us/intel-parallel-studio-xe) 18.0 and 19.0 GPU engine was validated on Ubuntu\* 18.04 with * GNU Compiler Collection 5.4 and 8.1 * Clang 3.8.1 * [Intel C/C++ Compiler](https://software.intel.com/en-us/intel-parallel-studio-xe) 19.0 * [Intel SDK for OpenCL applications](https://software.intel.com/en-us/intel-opencl) 2019 Update 3 * [Intel Graphics Compute Runtime for OpenCL](https://github.com/intel/compute-runtime/releases) 19.15.12831 on Windows Server 2019 with * Microsoft Visual C++ 14.0 (Visual Studio 2015 Update 3) * [Intel C/C++ Compiler](https://software.intel.com/en-us/intel-parallel-studio-xe) 19.0 * [Intel SDK for OpenCL applications](https://software.intel.com/en-us/intel-opencl) 2019 Update 3 * [Intel Graphics - Windows 10 DCH Drivers](https://downloadcenter.intel.com/download/28783/Intel-Graphics-Windows-10-DCH-Drivers) 26.20.100.6709 ## Requirements for Pre-built Binaries ### Linux Common dependencies: * GCC 4.8 or later Runtime specific dependencies: | Runtime configuration | Requirements | :-------------------- | :----------- | `cpu_gomp` | No additional requirements | `cpu_iomp` | Intel OpenMP runtime for Intel C/C++ Compiler 17.0 or later | `cpu_tbb` | Threading Building Blocks 2017 or later ### Windows Common dependencies: * Microsoft Visual C++ Redistributable 2015 or later Runtime specific dependencies: | Runtime configuration | Requirements | :-------------------- | :----------- | `cpu_vcomp` | No additional requirements | `cpu_iomp` | Intel OpenMP runtime for Intel C/C++ Compiler 17.0 or later | `cpu_tbb` | Threading Building Blocks 2017 or later ### macOS Common dependencies: * macOS 10.13 (High Sierra) or later Runtime specific dependencies: | Runtime configuration | Requirements | :-------------------- | :----------- | `cpu_iomp` | Intel OpenMP runtime for Intel C/C++ Compiler 17.0 or later | `cpu_tbb` | Threading Building Blocks 2017 or later -------- [Legal Information](doc/legal_information.md)