Transfer Learning for Performance Modeling of Configurable Systems: A Causal Analysis

Understand

Modern systems (e.g., deep neural networks, big data analytics, and compilers) are highly configurable, which means they expose different performance behavior under different configurations.

• The fundamental challenge is that one cannot simply measure all configurations due to the sheer size of the configuration space.
• Transfer learning has been used to reduce the measurement efforts by transferring knowledge about performance behavior of systems across environments.
• Previously, research has shown that statistical models are indeed transferable across environments.