This tutorial will cover the usage of OpenTuner, a open source framework for building domain-specific multi-objective program autotuners. OpenTuner supports fully customizable configuration representations, an extensible technique representation to allow for domain-specific techniques, and an easy to use interface for communicating with the tuned program. A key capability inside OpenTuner is the use of ensembles of disparate search techniques simultaneously. Techniques which perform well will receive larger testing budgets and techniques which perform poorly will be disabled. OpenTuner has been used by a number of different projects to build domain specific autotuners.

The topics covered in the workshop will be:

• Overview of autotuning: including a history of past autotuning projects and how autotuning is used today
• Machine learning primer: empirical search, model based techniques, and which technique is right for you
• OpenTuner framework: how is it designed and how you should use it
• Examples of using opentuner: presentations by current users of opentuner
• What makes a good search space representation: the secret sauce of autotuning
• How to go about autotuning your system with OpenTuner
• Hands-on session with OpenTuner