oneAPI Deep Neural Network Library (oneDNN)
Performance library for Deep Learning
2.2.0
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Build Options

oneDNN supports the following build-time options.

CMake Option Supported values (defaults in bold) Description
DNNL_LIBRARY_TYPE SHARED, STATIC Defines the resulting library type
DNNL_CPU_RUNTIME OMP, TBB, SEQ, THREADPOOL, DPCPPDefines the threading runtime for CPU engines
DNNL_GPU_RUNTIME NONE, OCL, DPCPP Defines the offload runtime for GPU engines
DNNL_BUILD_EXAMPLES ON, OFF Controls building the examples
DNNL_BUILD_TESTS ON, OFF Controls building the tests
DNNL_ARCH_OPT_FLAGS compiler flags Specifies compiler optimization flags (see warning note below)
DNNL_ENABLE_CONCURRENT_EXEC ON, OFF Disables sharing a common scratchpad between primitives in dnnl::scratchpad_mode::library mode
DNNL_ENABLE_JIT_PROFILING ON, OFF Enables integration with performance profilers
DNNL_ENABLE_PRIMITIVE_CACHE ON, OFF Enables primitive cache
DNNL_ENABLE_MAX_CPU_ISA ON, OFF Enables CPU dispatcher controls
DNNL_ENABLE_CPU_ISA_HINTS ON, OFF Enables CPU ISA hints
DNNL_VERBOSE ON, OFF Enables verbose mode
DNNL_AARCH64_USE_ACL ON, OFF Enables integration with Arm Compute Library for AArch64 builds
DNNL_BLAS_VENDOR NONE, ARMPL Defines an external BLAS library to link to for GEMM-like operations
DNNL_GPU_VENDOR INTEL, NVIDIA Defines GPU vendor for GPU engines

All other building options or values that can be found in CMake files are intended for development/debug purposes and are subject to change without notice. Please avoid using them.

Common options

CPU Options

Intel Architecture Processors and compatible devices are supported by oneDNN CPU engine. The CPU engine is built by default and cannot be disabled at build time.

Targeting Specific Architecture

oneDNN uses JIT code generation to implement most of its functionality and will choose the best code based on detected processor features. However, some oneDNN functionality will still benefit from targeting a specific processor architecture at build time. You can use DNNL_ARCH_OPT_FLAGS CMake option for this.

For Intel(R) C++ Compilers, the default option is -xSSE4.1, which instructs the compiler to generate the code for the processors that support SSE4.1 instructions. This option would not allow you to run the library on older processor architectures.

For GNU* Compilers and Clang, the default option is -msse4.1.

Warning
While use of DNNL_ARCH_OPT_FLAGS option gives better performance, the resulting library can be run only on systems that have instruction set compatible with the target instruction set. Therefore, ARCH_OPT_FLAGS should be set to an empty string ("") if the resulting library needs to be portable.

Runtime CPU dispatcher control

oneDNN JIT relies on ISA features obtained from the processor it is being run on. There are situations when it is necessary to control this behavior at run-time to, for example, test SSE4.1 code on an AVX2-capable processor. The DNNL_ENABLE_MAX_CPU_ISA build option controls the availability of this feature. See CPU Dispatcher Control for more information.

Runtime CPU ISA hints

For performance reasons, sometimes oneDNN JIT needs to be provided with extra hints so as to prefer or avoid particular CPU ISA feature. For example, one might want to disable Zmm registers usage in order to take advantage of higher clock speed. The DNNL_ENABLE_CPU_ISA_HINTS build option makes this feature available at runtime. See CPU ISA Hints for more information.

Runtimes

CPU engine can use OpenMP, Threading Building Blocks (TBB) or sequential threading runtimes. OpenMP threading is the default build mode. This behavior is controlled by the DNNL_CPU_RUNTIME CMake option.

OpenMP

oneDNN uses OpenMP runtime library provided by the compiler.

Warning
Because 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 linked to an executable may lead to undefined behavior including incorrect results or crashes. However as long as both the library and the application use the same or compatible compilers there would be no conflicts.

Threading Building Blocks (TBB)

To build oneDNN with TBB support, set DNNL_CPU_RUNTIME to TBB:

$ cmake -DDNNL_CPU_RUNTIME=TBB ..

Optionally, set the TBBROOT environmental variable to point to the TBB installation path or pass the path directly to CMake:

$ cmake -DDNNL_CPU_RUNTIME=TBB -DTBBROOT=/opt/intel/path/tbb ..

oneDNN has functional limitations if built with TBB:

  • Winograd convolution algorithm is not supported for fp32 backward by data and backward by weights propagation.

Threadpool

To build oneDNN with support for threadpool threading, set DNNL_CPU_RUNTIME to THREADPOOL

$ cmake -DDNNL_CPU_RUNTIME=THREADPOOL ..

The _DNNL_TEST_THREADPOOL_IMPL CMake variable controls which of the three threadpool implementations would be used for testing: STANDALONE, TBB, or EIGEN. The latter two require also passing TBBROOT or Eigen3_DIR paths to CMake. For example:

$ cmake -DDNNL_CPU_RUNTIME=THREADPOOL -D_DNNL_TEST_THREADPOOL_IMPL=EIGEN -DEigen3_DIR=/path/to/eigen/share/eigen3/cmake ..

Threadpool threading support is experimental and has the same limitations as TBB plus more:

  • As threadpools are attached to streams which are only passed during primitive execution, work decomposition is performed statically at the primitive creation time. At the primitive execution time, the threadpool is responsible for balancing the static decomposition from the previous item across available worker threads.

AArch64 Options

oneDNN includes experimental support for Arm 64-bit Architecture (AArch64). By default, AArch64 builds will use the reference implementations throughout. The following options enable the use of AArch64 optimised implementations for a limited number of operations, provided by AArch64 libraries.

AArch64 build configuration CMake Option Environment variables Dependencies
Arm Compute Library based primitives DNNL_AARCH64_USE_ACL=ON ACL_ROOT_DIR=*Arm Compute Library location* Arm Compute Library
Vendor BLAS library support DNNL_BLAS_VENDOR=ARMPL None Arm Performance Libraries

Arm Compute Library

Arm Compute Library is an open-source library for machine learning applications. The development repository is available from mlplatform.org, and releases are also available on GitHub. The DNNL_AARCH64_USE_ACL CMake option is used to enable Compute Library integration:

$ cmake -DDNNL_AARCH64_USE_ACL=ON ..

This assumes that the environment variable ACL_ROOT_DIR is set to the location of Arm Compute Library, which must be downloaded and built independently of oneDNN.

Warning
For a debug build of oneDNN it is advisable to specify a Compute Library build which has also been built with debug enabled.
oneDNN is only compatible with Compute Library builds v20.11 or later.

Vendor BLAS libraries

oneDNN can use a standard BLAS library for GEMM operations. The DNNL_BLAS_VENDOR build option controls BLAS library selection, and defaults to NONE. For AArch64 builds with GCC, use the Arm Performance Libraries:

$ cmake -DDNNL_BLAS_VENDOR=ARMPL ..

Additional options available for development/debug purposes. These options are subject to change without notice, see `cmake/options.cmake` for details.

GPU Options

Intel Processor Graphics is supported by oneDNN GPU engine. GPU engine is disabled in the default build configuration.

Runtimes

To enable GPU support you need to specify the GPU runtime by setting DNNL_GPU_RUNTIME CMake option. The default value is "NONE" which corresponds to no GPU support in the library.

OpenCL*

OpenCL runtime requires Intel(R) SDK for OpenCL* applications. You can explicitly specify the path to the SDK using -DOPENCLROOT CMake option.

$ cmake -DDNNL_GPU_RUNTIME=OCL -DOPENCLROOT=/path/to/opencl/sdk ..