These examples demonstrate how to build an application configured to use a multi-threaded scheduler. This application creates a user-controlled number of delay operators that can be run in parallel. If run in a single thread, the application's duration is approximately the sum of the delays of the individual delay operators. When all operators are run in parallel, the duration is roughly equal to the longest duration of any of the individual operators.

Visit the SDK User Guide to learn more about the Multi-threaded scheduler.

This example shows a simple application using only native operators. There are three operators involved:

1. a transmitter, set to transmit the integer value 0 on port "out"
2. a set of delay operators that prints the received value, waits a specified delay, and then increments by a user-specified value. The incremented value is then transmitted.
3. a receiver that prints the sum of all received values

The user can configure the number of delay operators via the num_delay_op parameter in app_config.yaml. The first of these will have a delay equal to the value of delay in app_config.yaml. The jth one of these will have the value delay + i * delay_step where delay_step is also set in app_config.yaml.

The number of workers used by the multi-threaded scheduler is controlled by the worker_thread_number parameter in app_config.yaml.

Data Flow Tracking is also optionally enabled by changing the tracking field in the YAML file. It is set to false by default. Other options in the YAML include a silent option to suppress verbose output from the operators (false by default) and a count option that can be used to control how many messages are sent from the transmitter (1 by default).

Built with the SDK, see instructions from the top level README.

First, go in your build or install directory (automatically done by ./run launch).

Set values for num_operator, delay, delay_step and worker_thread_number as desired in app_config.yaml.

Then, run:

./examples/multithread/cpp/multithread

For the C++ application, the scheduler to be used can be set via the scheduler entry in ping_async.yaml. It defaults to event_based (an event-based multithread scheduler), but can also be set to either multi_thread (polling-based) or greedy (single thread).

• multithread.py: This example demonstrates how to configure and use a multi-threaded scheduler instead of the default single-threaded one. It involves three operators as described for the C++ API example described above. The primary difference is that instead of using a YAML file for the configuration variables, all values are set via the command line. Call the script below with the --help option to get a full description of the command line parameters. By default a polling-based multithread scheduler will be used, but if --event_based is specified, the event-based multithread scheduler will be used instead.

Built with the SDK, see instructions from the top level README.

First, go in your build or install directory (automatically done by ./run launch).

Then, run the app with the options of your choice. For example, to use 8 worker threads to run 32 delay operators with delays ranging linearly from 0.2 to (0.2 + 0.05 * 31), one would set:

python3 ./examples/multithread/python/multithread.py --threads 8 --num_delay_ops 32 --delay 0.2 --delay_step 0.05 --event_based