Optimizing memory usage


Before applying any of the recommendations provided here, note that it may significantly impact first inference latency.

The most RAM-consuming OpenVINO stage is model compilation. It may cause several issues:

  • Not enough memory to compile a model. To decrease memory requirement, the following options may be applied:

    • Weights mapping - memory mapping (using mmap) has been introduced as the default way to work with weights. Currently, this feature is supported by the IR and ONNX frontends. Mapping may be switched by specifying the ov::enable_mmap(BOOL) property for the ov::Core. Because of its “memory-on-demand” nature, there is no need to store all weights in RAM. Storing just the data that is needed at the moment lowers the amount of memory required for compilation. Moreover, mmap provides extensive memory sharing, so the consecutive compilation of the same model will fetch the information already stored in RAM instead of reading it one more time from storage.

    • Decrease the number of threads for compilation - to change the number of threads, specify the ov::compilation_num_threads(NUMBER) property for the ov::Core or pass it as an additional argument to ov::Core::compile_model()

  • Not enough memory to recompile a model. If model compilation is successful but one of the following recompilations fails due lack of resources, it may be caused by:

    • Memory leak - to determine direct leaks, you can use tools like ‘address-sanitizer’ or ‘valgrind’. In case of indirect leaks, which cannot be caught by tools, peak RAM (VMHWM) may be tracked (you can use tests/stress_tests/memleaks_tests as a tracking tool). If you experience significant memory usage increase, report it in Github “Issues”

    • Memory allocator behavior - each allocator works according to a unique strategy and balances between performance and memory usage. For example, the GNU allocator aggressively requests from the OS for more memory for consecutive model compilations than was required for the first compilation (such behavior may be determined by tracking actual RAM (VMRSS) after compilation - it will grow until some stable point). To optimize memory pressure, the following options are available:

      • Apply malloc_trim(0). The function attempts to release free memory even from thread caches, so it may signifficantly decrease and stabilize VMRSS usage

      • Use glibc Tunables. A couple of promising options are: glibc.malloc.trim_threshold and glibc.malloc.arena_max. More details on the two may be found in the GNU Tunables Manual

      • Try another allocator. One of the allocators that handles memory carefully is jemalloc