Note
Starting with version 1.1 the library is renamed to DNNL. Please read Intel MKL-DNN to DNNL Transition Guide.
Note
Version 1.0 brings incompatible changes to the 0.20 version. Please read Version 1.0 Transition Guide.
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
- BigDL
- Caffe* Optimized for Intel Architecture
- Chainer*
- DeepLearning4J*
- Intel Nervana Graph
- MATLAB* Deep Learning Toolbox
- Menoh*
- Microsoft* Cognitive Toolkit (CNTK)
- ONNX Runtime
- OpenVINO(TM) toolkit
- PaddlePaddle*
- PyTorch*
- Tensorflow*
Pre-built binaries for Linux*, Windows*, and macOS* are available for download in the releases section. Package names use the following convention:
| OS | Package name |
|---|---|
| Linux | dnnl_lnx_<version>_cpu_<cpu runtime>[_gpu_<gpu runtime>].tgz |
| Windows | dnnl_win_<version>_cpu_<cpu runtime>[_gpu_<gpu runtime>].zip |
| macOS | dnnl_mac_<version>_cpu_<cpu runtime>.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 section below and the Build Options section in the developer guide for more details on CPU and GPU runtimes.
If the configuration you need is not available, you can build the library from source.
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.
- Ensure that the changes are consistent with the code contribution guidelines and coding style.
- Ensure that you can build the product and run all the examples with your patch.
- Submit a pull request.
For additional details, see contribution guidelines.
This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the Contributor Covenant code of conduct.
Please submit your questions, feature requests, and bug reports on the GitHub issues page.
You may reach out to project maintainers privately at dnnl.maintainers@intel.com.
WARNING
The following functionality has preview status and might be changed without prior notification in future releases:
DNNL is licensed under Apache License Version 2.0. This software includes the following third-party components:
- Xbyak distributed under 3-clause BSD licence
- gtest distributed under 3-clause BSD license
- ittnotify distributed under 3-clause BSD license
- Cmake distributed under 3-clause BSD license
- MathJax distributed under Apache License Version 2.0
- Boost C++ Libraries distributed under Boost Software License, Version 1.0
- Developer guide explains programming model, supported functionality, details of primitives implementations and includes annotated examples.
- API reference provides comprehensive reference of the library API.
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
DNNL supports systems meeting the following requirements:
- Operating sytem with Intel 64 architecture support
- C++ compiler with C++11 standard support
- CMake 2.8.11 or later
- Doxygen 1.8.5 or later to build documentation
Configurations of CPU and GPU engines may introduce additional build time dependencies.
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) 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.
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
When DNNL is built from source, the library runtime dependencies and specific versions are defined by the build environment.
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) |
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) |
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) |
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 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 17.0 and 19.0
on macOS 10.13 (High Sierra) with
- Apple LLVM version 9.2 (XCode 9.2)
- Intel C/C++ Compiler 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 19.0
- Intel SDK for OpenCL applications 2019 Update 3
- Intel Graphics Compute Runtime for OpenCL 19.15.12831
on Windows Server 2019 with
- Microsoft Visual C++ 14.0 (Visual Studio 2015 Update 3)
- Intel C/C++ Compiler 19.0
- Intel SDK for OpenCL applications 2019 Update 3
- Intel Graphics - Windows 10 DCH Drivers 26.20.100.6709
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 |
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 |
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 |