AutoMOMML: Automatic Multi-Objective Modeling with Machine Learning

Event Sponsor: 
Argonne Leadership Computing Facility Seminar
Start Date: 
Jun 14 2016 - 12:00pm
Building/Room: 
Building 241/Room D172
Location: 
Argonne National Laboratory
Speaker(s): 
Prasanna Balaprakash
Speaker(s) Title: 
Argonne National Laboratory, LCF (Performance Engineering)

In recent years, automatic data-driven modeling with machine learning (ML) has received considerable attention as an alternative to analytical modeling for many modeling tasks. While ad hoc adoption of ML approaches has obtained success, the real potential for automation in data-driven modeling has yet to be achieved. In this talk, we will describe AutoMOMML, an end-to-end, ML-based framework to build predictive models for objectives such as performance, and power. 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 hardware counters as application signatures show that the median prediction error of performance, processor power, and DRAM power models are 13%, 2.3%, and 8%, respectively.

Miscellaneous Information: 

Upcoming Presenters:

July 19 - User Experience
Aug 9 - Janet Knowles - Apps for ALCF