Deep Neural Network Library (DNNL)  1.2.0
Performance library for Deep Learning
CPU dispatcher control

DNNL uses JIT code generation to implement most of its functionality and will choose the best code based on detected processor features. Sometimes it is necessary to control which features DNNL detects. This is sometimes useful for debugging purposes or for performance exploration. To enable this, DNNL provides two mechanisms: an environment variable DNNL_MAX_CPU_ISA and a function dnnl::set_max_cpu_isa().

The environment variable can be set to an upper-case name of the ISA as defined by the dnnl::cpu_isa enumeration. For example, DNNL_MAX_CPU_ISA=AVX2 will instruct DNNL to never dispatch to JIT-ed CPU primitive implementations that require ISA 'higher' than AVX2 like AVX512. The DNNL_MAX_CPU_ISA=ALL setting implies no restrictions.

The dnnl::set_max_cpu_isa() function allows changing the ISA at run-time. The limitation is that, it is possible to set the value only before the first JIT-ed function is generated. This limitation ensures that the JIT-ed code observe consistent CPU features both during generation and execution.

This feature can be enabled or disabled at build time. See Build Options for more information.