Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an open source performance library for Deep Learning (DL) applications intended for acceleration of DL frameworks on Intel(R) architecture. Intel(R) MKL-DNN includes highly vectorized and threaded building blocks for implementation of convolutional neural networks (CNN) with C and C++ interfaces. We created this project to enable the DL community to innovate on Intel(R) processors.
Intel MKL-DNN includes functionality similar to Intel(R) Math Kernel Library (Intel(R) MKL) 2017, but is not API compatible. We are investigating how to unify the APIs in future Intel MKL releases.
This release contains a range of performance critical functions used in modern image recognition topologies including Cifar*, AlexNet*, VGG*, GoogleNet* and ResNet* optimized for wide range of Intel processors.
Functionality related to integer data types s16s16s32 and u8s8u8 included
in this release is experimental and might change without prior notification in
future releases.
Intel MKL-DNN is licensed under Apache License Version 2.0.
The latest version of Intel MKL-DNN reference manual is 9401 available GitHub pages. Basic concepts are also explained in the tutorial
Please report issues and suggestions via GitHub issues or start a topic on Intel MKL forum.
We welcome community contributions to Intel MKL-DNN. If you have an idea how to improve the library:
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Share your proposal via GitHub issues.
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Ensure you can build the product and run all the examples with your patch
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In the case of a larger feature, create a test
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Submit a pull request
We will review your contribution and, if any additional fixes or modifications are necessary, may provide feedback to guide you. When accepted, your pull request will be merged into our internal and GitHub repositories.
Intel MKL-DNN supports Intel(R) 64 architecture processors and is optimized for
- Intel Atom(R) processor with Intel(R) SSE4.1 support
- 4th, 5th, 6th and 7th generation Intel(R) Core processor
- Intel(R) Xeon(R) processor E5 v3 family (code named Haswell)
- Intel(R) Xeon(R) processor E5 v4 family (code named Broadwell)
- Intel(R) Xeon(R) Platinum processor family (code name Skylake)
- Intel(R) Xeon Phi(TM) product family x200 (code named Knights Landing)
- Future Intel(R) Xeon Phi(TM) processor (code named Knights Mill)
The software dependencies are:
The software was validated on RedHat* Enterprise Linux 7 with
- GNU* Compiler Collection 4.8
- GNU* Compiler Collection 6.1
- Clang* 3.8.0
- Intel(R) C/C++ Compiler 17.0
and on Windows Server* 2012 R2 with
- Visual Studio* 2015
- Intel(R) C/C++ Compiler 17.0
The implementation uses OpenMP* 4.0 SIMD extensions. We recommend using Intel(R) Compiler for the best performance results.
Download Intel MKL-DNN source code or clone the repository to your system
git clone https://github.com/01org/mkl-dnn.git
Ensure that all software dependencies are in place and have at least minimal supported version.
Intel MKL-DNN can take advantage of optimized matrix-matrix multiplication (GEMM) function from Intel MKL. The dynamic library with this functionality is included in the repository. If you choose to build Intel MKL-DNN with the binary dependency download Intel MKL small libraries using provided script
cd scripts && ./prepare_mkl.sh && cd ..
or manually from GitHub release section
and unpack it to the external directory in the repository root.
You 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.
Intel MKL-DNN uses a CMake-based build system
mkdir -p build && cd build && cmake .. && make
Intel MKL-DNN includes unit tests implemented using the googletest framework. To validate your build, run:
make test
Documentation is provided inline and can be generated in HTML format with Doxygen:
make doc
Documentation will reside in build/reference/html folder.
Finally,
make install
will place the header files, libraries and documentation in /usr/local. To change
the installation path, use the option -DCMAKE_INSTALL_PREFIX=<prefix> when invoking CMake.
Intel MKL-DNN include several header files providing C and C++ APIs for the functionality and several dynamic libraries depending on how Intel MKL-DNN was built. Intel OpenMP runtime and Intel MKL small libraries are not installed for standalone Intel MKL-DNN build.
| File | Description |
|---|---|
| lib/libmkldnn.so | Intel MKL-DNN dynamic library |
| lib/libiomp5.so | Intel OpenMP* runtime library |
| lib/libmkl_gnu.so | Intel MKL small library for GNU* OpenMP runtime |
| lib/libmkl_intel.so | Intel MKL small library for Intel(R) OpenMP runtime |
| include/mkldnn.h | C header |
| include/mkldnn.hpp | C++ header |
| include/mkldnn_types.h | auxillary C header |
Intel MKL-DNN uses OpenMP* for parallelism and requires an OpenMP runtime library to work. As different OpenMP runtimes may not be binary compatible it's important to ensure that only one OpenMP runtime is used throughout the application. Having more than one OpenMP runtime initialized may lead to undefined behavior resulting in incorrect results or crashes.
Intel MKL-DNN library built with binary dependency will link against Intel OpenMP runtime included with Intel MKL small libraries package. Intel OpenMP runtime is binary compatible with GNU OpenMP and CLANG OpenMP runtimes and should be used in the final application. Here are example linklines for GNU C++ compiler and Intel C++ compiler.
g++ -std=c++11 -fopenmp -Wl,--as-needed -I${MKLDNNROOT}/include -L${MKLDNNROOT}/lib simple_net.cpp -lmkldnn -lmklml_intel -liomp5
icpc -std=c++11 -qopenmp -I${MKLDNNROOT}/include -L${MKLDNNROOT}/lib simple_net.cpp -lmkldnn -lmklml_intel
In g++ example option -Wl,--as-needed forces linker to resolve OpenMP symbols
in Intel OpenMP runtime library.
Intel MKL-DNN library built standalone will use OpenMP runtime supplied by the compiler, so as long as both the library and the application use the same compiler correct OpenMP runtime will be used.
g++ -std=c++11 -fopenmp -I${MKLDNNROOT}/include -L${MKLDNNROOT}/lib simple_net.cpp -lmkldnn
icpc -std=c++11 -qopenmp -I${MKLDNNROOT}/include -L${MKLDNNROOT}/lib simple_net.cpp -lmkldnn