OTHERS.md

Unicorn usage on a different dataset

To run Unicorn on a different dataset you will only need /tests/run_unicorn_debug.py and /tests/run_unicorn_optimization.py. To perform interventions using the recommended configuration you need to develop your own utilities (similar to /services/run_params.py). Additionally, you need to make some changes in the etc/config.yml to use the configuration options and their values accordingly. The necessary steps are the following:

Steps

• Update init_dir in config.yml with the directory where initial data is stored.

• Update bug_dir in config.yml with the directory where bug data is stored.

• Update output_dir variable in the config.yml file where you want to save the output data.

• Update hardware_columns in the config.yml with the hardware configuration options you want to use.

• Update kernel_columns in the config.yml with the kernel configuration options you want to use.

• Update perf_columns in the config.yml with the events you want to track using perf. If you use any other monitoring tool you need to update it accordingly.

• Update measurement_colums in the config.yml based on the performance objectives you want to use for bug resolve.

• Update is_intervenable variables in the config.yml with the configuration options you want to use and based on your application change their values to True or False. True indicates the configuration options can be intervened upon and vice-versa for False.

• Update the option_values variables in the config.yml based on the allowable values your option can take.

• Now, you can run run_unicorn_debug.py and run_unicorn_optimization.py with your own specification. Please notice that you also need to update the directories according to your software and hardware name in data directory. If you change the name of the variables in the config file or use a new config file you need to make changes accordingly from in run_unicorn_debug.py and run_unicorn_optimization.py.