High-level Performance Hints

Even though all supported devices in OpenVINO™ offer low-level performance settings, utilizing them is not recommended outside of very few cases. The preferred way to configure performance in OpenVINO Runtime is using performance hints. This is a future-proof solution fully compatible with the automatic device selection inference mode and designed with portability in mind.

The hints also set the direction of the configuration in the right order. Instead of mapping the application needs to the low-level performance settings, and keeping an associated application logic to configure each possible device separately, the hints express a target scenario with a single config key and let the device configure itself in response.

Previously, a certain level of automatic configuration was the result of the default values of the parameters. For example, the number of CPU streams was deduced from the number of CPU cores, when ov::streams::AUTO (CPU_THROUGHPUT_AUTO in the pre-API 2.0 terminology) was set. However, the resulting number of streams did not account for actual compute requirements of the model to be inferred. The hints, in contrast, respect the actual model, so the parameters for optimal throughput are calculated for each model individually (based on its compute versus memory bandwidth requirements and capabilities of the device).

Performance Hints: Latency and Throughput

As discussed in the Optimization Guide there are a few different metrics associated with inference speed. Throughput and latency are some of the most widely used metrics that measure the overall performance of an application.

Therefore, in order to ease the configuration of the device, OpenVINO offers two dedicated hints, namely ov::hint::PerformanceMode::THROUGHPUT and ov::hint::PerformanceMode::LATENCY. A special ov::hint::PerformanceMode::UNDEFINED hint acts the same as specifying no hint.

For more information on conducting performance measurements with the benchmark_app, refer to the last section in this document.

Keep in mind that a typical model may take significantly more time to load with the ov::hint::PerformanceMode::THROUGHPUT and consume much more memory, compared to the ov::hint::PerformanceMode::LATENCY. Also, the THROUGHPUT and LATENCY hints only improve performance in an asynchronous inference pipeline. For information on asynchronous inference, see the Prefer Async API section of this document.

Performance Hints: How It Works

Internally, every device “translates” the value of the hint to the actual performance settings. For example, the ov::hint::PerformanceMode::THROUGHPUT selects the number of CPU or GPU streams. Additionally, the optimal batch size is selected for the GPU and the automatic batching is applied whenever possible. To check whether the device supports it, refer to the devices/features support matrix article.

The resulting (device-specific) settings can be queried back from the instance of the ov:Compiled_Model.

Be aware that the benchmark_app outputs the actual settings for the THROUGHPUT hint. See the example of the output below:

$benchmark_app -hint tput -d CPU -m 'path to your favorite model'
[Step 8/11] Setting optimal runtime parameters
[ INFO ] Device: CPU
[ INFO ]   { NUM_STREAMS , 4 }

Using the Performance Hints: Basic API

In the example code snippet below, ov::hint::PerformanceMode::THROUGHPUT is specified for the ov::hint::performance_mode property for compile_model :

auto compiled_model = core.compile_model(model, "GPU",
compiled_model = core.compile_model(model, "GPU", config)

Additional (Optional) Hints from the App

For an application that processes 4 video streams, the most future-proof way to communicate the limitation of the parallel slack is to equip the performance hint with the optional ov::hint::num_requests configuration key set to 4. As mentioned earlier, this will limit the batch size for the GPU and the number of inference streams for the CPU. Thus, each device uses the ov::hint::num_requests while converting the hint to the actual device configuration options:

// limiting the available parallel slack for the 'throughput' hint via the ov::hint::num_requests
// so that certain parameters (like selected batch size) are automatically accommodated accordingly 
auto compiled_model = core.compile_model(model, "GPU",
# limiting the available parallel slack for the 'throughput'
# so that certain parameters (like selected batch size) are automatically accommodated accordingly 
compiled_model = core.compile_model(model, "GPU", config)

Optimal Number of Inference Requests

The hints are used on the presumption that the application queries ov::optimal_number_of_infer_requests to create and run the returned number of requests simultaneously:

// when the batch size is automatically selected by the implementation
// it is important to query/create and run the sufficient #requests
auto compiled_model = core.compile_model(model, "GPU",
auto num_requests = compiled_model.get_property(ov::optimal_number_of_infer_requests);
# when the batch size is automatically selected by the implementation
# it is important to query/create and run the sufficient requests
compiled_model = core.compile_model(model, "GPU", config)
num_requests = compiled_model.get_property("OPTIMAL_NUMBER_OF_INFER_REQUESTS")

While an application is free to create more requests if needed (for example to support asynchronous inputs population) it is very important to at least run the ov::optimal_number_of_infer_requests of the inference requests in parallel. It is recommended for efficiency, or device utilization, reasons.

Keep in mind that ov::hint::PerformanceMode::LATENCY does not necessarily imply using single inference request. For example, multi-socket CPUs can deliver as many requests at the same minimal latency as the number of NUMA nodes in the system. To make your application fully scalable, make sure to query the ov::optimal_number_of_infer_requests directly.

Prefer Async API

The API of the inference requests offers Sync and Async execution. The ov::InferRequest::infer() is inherently synchronous and simple to operate (as it serializes the execution flow in the current application thread). The Async “splits” the infer() into ov::InferRequest::start_async() and ov::InferRequest::wait() (or callbacks). For more information on synchronous and asynchronous modes, refer to the OpenVINO Inference Request documentation.

Although the synchronous API can be easier to start with, it is recommended to use the asynchronous (callbacks-based) API in production code. It is the most general and scalable way to implement the flow control for any possible number of requests. The THROUGHPUT and LATENCY performance hints automatically configure the Asynchronous pipeline to use the optimal number of processing streams and inference requests.


Important: Performance Hints only work when asynchronous execution mode is used. They do not affect the performance of a synchronous pipeline.

Combining the Hints and Individual Low-Level Settings

While sacrificing the portability to some extent, it is possible to combine the hints with individual device-specific settings. For example, use ov::hint::PerformanceMode::THROUGHPUT to prepare a general configuration and override any of its specific values:

    // high-level performance hints are compatible with low-level device-specific settings 
auto compiled_model = core.compile_model(model, "CPU",
          "INFERENCE_NUM_THREADS": "4"}
# limiting the available parallel slack for the 'throughput'
# so that certain parameters (like selected batch size) are automatically accommodated accordingly
compiled_model = core.compile_model(model, "CPU", config)

Testing Performance of the Hints with the Benchmark_App

The benchmark_app, that exists in both C++ and Python versions, is the best way to evaluate the functionality of the performance hints for a particular device:

  • benchmark_app -hint tput -d ‘device’ -m ‘path to your model’

  • benchmark_app -hint latency -d ‘device’ -m ‘path to your model’

Disabling the hints to emulate the pre-hints era (highly recommended before trying the individual low-level settings, such as the number of streams as below, threads, etc):

  • benchmark_app -hint none -nstreams 1 -d ‘device’ -m ‘path to your model’

Additional Resources