AutoMOMML: Automatic Multi-Objective Modeling with Machine Learning
|Title||AutoMOMML: Automatic Multi-Objective Modeling with Machine Learning|
|Year of Publication||2015|
|Authors||Balaprakash, P, Tiwari, A, Wild, SM, Carrington, L, Hovland, PD|
In recent years, automatic data-driven modeling with machine learning has received considerable attention as an alternative to analytical modeling for many modeling tasks. While ad hoc adoption of machine learning approaches has ob- tained success, the real potential for automation in data-driven modeling has yet to be achieved. We propose AutoMOMML, an end-to-end, machine-learning-based framework to build predictive models for objectives such as performance, power, and energy. The framework adopts statistical approaches to reduce the modeling complexity and automatically identifies and configures the most suitable learning algorithm to model the required objectives based on hardware and application signatures. The experimental results using PAPI hardware counters as these signatures show that the median prediction error of performance, processor power, and DRAM power models are 13%, 2.3%, and 8%, respectively.