Deep Neural Network Library (DNNL)  1.2.0
Performance library for Deep Learning
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Primitive Cache
The primitive cache is disabled in the default build configuration (see Build Options).

Primitive creation time largely depends on the underlying implementation, for instance, DNNL uses just-in-time compilation (JIT) to generate optimal code for some CPU and GPU implementations, which introduces overhead.

To mitigate primitive creation overhead, DNNL provides the primitive cache which automatically caches created primitives to avoid repeating JIT compilation for the primitives with identical operation descriptors, attributes, underlying primitive implementations, etc. It can significantly reduce primitive creation overhead, especially when an application or a framework creates primitives for every instance of inference or iteration of training process.

Each engine has independent primitive cache. Since the engine and its primitive cache have the same lifetime a user should reuse the engine to benefit from the primitive cache.

Memory consumption

Since the primitive cache has limited capacity, it uses LRU (Least Recently Used) replacement policy to evict excess primitives. The capacity indicates the maximum number of primitives it can hold at a time and it can be adjusted with an environment variable DNNL_PRIMITIVE_CACHE_CAPACITY. The default capacity is 200. If the capacity is 0 then the primitve cache is disabled.


Primitive cache is an experimental feature. No API to control its behavior is provided.

Primitive cache profiling

Information about primitive cache hits and misses can be used for debug purposes. That information is part of the verbose output for verbose level 2 (Verbose Mode).