1. CISC 879 - Machine Learning for Solving Systems Problems Microarchitecture Design Space Exploration Lecture 4 John Cavazos Dept of Computer & Information Sciences University of Delaware www.cis.udel.edu/~cavazos/cisc879

2. CISC 879 - Machine Learning for Solving Systems Problems Recent ARM Processor Increasingly large number of interesting design points.

3. CISC 879 - Machine Learning for Solving Systems Problems Architecture Simulation  Cycle-accurate simulation – Accurately captures trends in design space – Estimates various metrics (e.g., power, performance)  Challenges with simulation – Accurate simulation very slow – Number of simulations grows very quickly with number of parameters (e.g., cache size, issue width) considered

4. CISC 879 - Machine Learning for Solving Systems Problems Why do Predictive Modeling?  Exploring architectural design spaces is hard – Accurate simulation very slow – Number of simulations grows very quickly with number of parameters (e.g., cache size, issue width) considered  With Predictive Modeling – Small number of simulations to train a model, rest of space is predicted – Even smaller number with cross-program prediction!

5. CISC 879 - Machine Learning for Solving Systems Problems Speeding up simulation  Reduce Input Sizes – Reduces costs of simulation with smaller inputs  Reduce Instructions Simulated – Sampling of instructions (“hot code”) – Sampled trace from phases  Reduce Simulated Configurations – Sample small number of points from design space

6. CISC 879 - Machine Learning for Solving Systems Problems Predictive Modeling  Effectively use sparsely sampled simulated design space  Uses simulated parts of space as training data  Models predict metric of interest (e.g., performance, energy) 1.45

7. CISC 879 - Machine Learning for Solving Systems Problems Digression into Regression Suppose you have a set of data (x i,y i ) and you want to see if a linear relationship exists between x and y. y = mx + b

8. CISC 879 - Machine Learning for Solving Systems Problems Regression with 1 variable Source: http://en.wikipedia.org/wiki/Linear_regression

9. CISC 879 - Machine Learning for Solving Systems Problems Linear Regression Source: http://www.stanford.edu/~bcclee/documents/lee2006-asplos-slides.pdf

10. CISC 879 - Machine Learning for Solving Systems Problems Applying Predictive Models ► Inputs ► Architecture configuration ► Outputs ► Metric to predict ► E.g., performance relative to a “baseline”

11. CISC 879 - Machine Learning for Solving Systems Problems Inputs Source: http://www.stanford.edu/~bcclee/documents/lee2006-asplos-slides.pdf

12. CISC 879 - Machine Learning for Solving Systems Problems Experimental Methodology Source: http://www.stanford.edu/~bcclee/documents/lee2006-asplos-slides.pdf

13. CISC 879 - Machine Learning for Solving Systems Problems Model Validation Source: http://www.stanford.edu/~bcclee/documents/lee2006-asplos-slides.pdf

14. CISC 879 - Machine Learning for Solving Systems Problems Regional Sampling Source: http://www.stanford.edu/~bcclee/documents/lee2006-asplos-slides.pdf

15. CISC 879 - Machine Learning for Solving Systems Problems Performance Prediction Source: http://www.stanford.edu/~bcclee/documents/lee2006-asplos-slides.pdf

16. CISC 879 - Machine Learning for Solving Systems Problems Power Prediction Source: http://www.stanford.edu/~bcclee/documents/lee2006-asplos-slides.pdf

17. CISC 879 - Machine Learning for Solving Systems Problems Tools Available CORE :: Comprehensive Optimization via Regression Estimates Architecture DSE data sets Statistical scripts to perform analysis http://www.stanford.edu/~bcclee/software.html

18. CISC 879 - Machine Learning for Solving Systems Problems Tools Available (cont’d) Fusion Predictive Modeling Tools Tools for application performance prediction Available upon request http://fusion.csl.cornell.edu/tools/fpmt.html

19. CISC 879 - Machine Learning for Solving Systems Problems Conclusions Source: http://www.stanford.edu/~bcclee/documents/lee2006-asplos-slides.pdf