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CISC 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
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CISC 879 - Machine Learning for Solving Systems Problems
Microarchitecture Design Space Exploration
Lecture 4
John CavazosDept of Computer & Information Sciences
University of Delaware
www.cis.udel.edu/~cavazos/cisc879
CISC 879 - Machine Learning for Solving Systems Problems
Recent ARM Processor
Increasingly large number of interesting design points.
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
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!
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
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
CISC 879 - Machine Learning for Solving Systems Problems
Digression into Regression
Suppose you have a set of data (xi,yi) and you want to see if a linear relationship exists between x and y. y = mx + b
CISC 879 - Machine Learning for Solving Systems Problems
Regression with 1 variable
Source: http://en.wikipedia.org/wiki/Linear_regression
CISC 879 - Machine Learning for Solving Systems Problems
Linear Regression
Source: http://www.stanford.edu/~bcclee/documents/lee2006-asplos-slides.pdf
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”
CISC 879 - Machine Learning for Solving Systems Problems
Inputs
Source: http://www.stanford.edu/~bcclee/documents/lee2006-asplos-slides.pdf
CISC 879 - Machine Learning for Solving Systems Problems
Experimental Methodology
Source: http://www.stanford.edu/~bcclee/documents/lee2006-asplos-slides.pdf
CISC 879 - Machine Learning for Solving Systems Problems
Model Validation
Source: http://www.stanford.edu/~bcclee/documents/lee2006-asplos-slides.pdf
CISC 879 - Machine Learning for Solving Systems Problems
Regional Sampling
Source: http://www.stanford.edu/~bcclee/documents/lee2006-asplos-slides.pdf
CISC 879 - Machine Learning for Solving Systems Problems
Performance Prediction
Source: http://www.stanford.edu/~bcclee/documents/lee2006-asplos-slides.pdf
CISC 879 - Machine Learning for Solving Systems Problems
Power Prediction
Source: http://www.stanford.edu/~bcclee/documents/lee2006-asplos-slides.pdf
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
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
CISC 879 - Machine Learning for Solving Systems Problems
Conclusions
Source: http://www.stanford.edu/~bcclee/documents/lee2006-asplos-slides.pdf
