Deep Neural Network Library (DNNL)  1.2.0
Performance library for Deep Learning
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Build Options

DNNL supports the following build-time options.

Option Supported values (defaults in bold) Description
DNNL_LIBRARY_TYPE SHARED, STATIC Defines the resulting library type
DNNL_CPU_RUNTIME OMP, TBB, SEQ Defines the threading runtime for CPU engines
DNNL_GPU_RUNTIME NONE, OCL 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 Intel(R) VTune(TM) Amplifier
DNNL_ENABLE_PRIMITIVE_CACHE ON, OFF Enables primitive cache
DNNL_ENABLE_MAX_CPU_ISA ON, OFF Enables controlling CPU dispatcher at run-time

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

Common options

Primitive cache

Primitive cache is disabled in the default build configuration.

To enable the primitive cache you can use DNNL_ENABLE_PRIMITIVE_CACHE CMake option. The default value is "OFF".

CPU Options

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

Targeting Specific Architecture

DNNL uses JIT code generation to implement most of its functionality and will choose the best code based on detected processor features. However, some DNNL 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

DNNL 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.

Runtimes

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

OpenMP

DNNL 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.

TBB

To build DNNL 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 ..

DNNL has limited optimizations for Intel TBB and has some functional limitations if built with Intel TBB.

Functional limitations:

GPU Options

Intel Processor Graphics is supported by DNNLs 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 ..