1. Predictive Design Space Exploration Using Genetically Programmed Response Surfaces Henry Cook Department of Electrical Engineering and Computer Science University of California, Berkeley hcook@eecs.berkeley.edu Kevin Skadron Department of Computer Science University of Virginia skadron@cs.virginia.edu

2. Designs Are Growing More Complex More transistors = more complex designs Superscalar, out-of-order execution Chip multiprocessors, systems-on-a-chip Heterogeneity, shared memory, interconnects… Many interrelated design variables with emergent interactions Unclear whether analytic models can be constructed based on a priori understanding

3. How can we find the best design? Detailed, cycle-accurate simulations Testing every option is time consuming 1 min. x 1 billion simulations = too long! Even search requires too many simulations Solution: Predict which designs are best based on a small data sample

4. Why not heuristic search? Every step of search requires at least one full simulation How many steps? Predicting based on a predetermined sample becomes increasingly efficient as space grows Assuming predictions are accurate Assuming sample is small enough

5. How can we address complexity? We need a tool to predict global performance based on a data sample Many techniques might do this, but we want to use one that… Is more accurate when using small samples Finds the true optimal solutions Gives the architect more insight Not just ‘what’ the best answer is, but also ‘why’ it is the best

6. How can we make predictions? Build a ‘response surface’ A function that relates design choices to a performance measure Predicts performance for designs we did not ever simulate Must have high accuracy to be useful

7. A simple response surface Linear Regression:

8. A simple response surface Linear Regression:

9. A simple response surface Linear Regression:

10. A simple response surface Non-linear models are more precise:

11. How to use response surfaces The true performance data:

12. How to use response surfaces Step 1: Run simulations to make a sample

13. How to use response surfaces Step 2: Build the response surface

14. How to use response surfaces Step 3: Predict which designs are optimal

15. How can we build the surface? Linear/polynomial regression Artificial neural networks Genetic Programming Automated Robust Insightful

16. Genetic Programming Creates non-linear, polynomial functions which match sample data Previous uses Calcination of cement Stress fractures in steel Branch prediction strategy Evolutionary algorithm Natural selection Survival of the fittest

17. Remove lowest quality individuals Recombine best individuals Evaluate fitness of new individuals Evolution and reproduction Result: Explicit response surface functions which best match data

18. Evolution and reproduction Expression trees define the structure of the response surface equation Tuning parameters provide best possible fit to collected data Candidates are evaluated based on distance of predictions from sample after tuning

19. Combine Evolution and reproduction

20. Proving the GP technique Ipek et. al (ASPLOS-XII) 12 design choices, 20K+ possible designs Predicted IPC and cache performance of 11 applications Lee and Brooks (ASPLOS-XII) 13 design choices, 1 billion+ possible designs Predicted power and BIPS of 7 applications Took simulation data from previous studies Designs are realistic but (comparatively) simple

21. How good are the predictions? 0.1% of possible designs Branch prediction and L2 cache miss rate <1% mean error and <2% s.d. Instructions per cycle 2.8-4.9% mean error, 2.2-3.4% s.d. 0.000002% of possible designs Billions of instructions per second 1.1-6.1% mean, 2.3-9.9% s.d. Power (W) 3.5-6.2% mean, 3.4-5.7% s.d.

22. How good are the predictions? CDFs of prediction accuracy of BIPS/IPC

23. How good are the predictions? Global prediction accuracy of IPC performance for 11 applications. Sample size was only 0.25%.

24. How good are the predictions? CDFs of prediction accuracy for Ipek

25. How good are the predictions? CDFs of prediction accuracy of BIPS for Lee

26. How good are the predictions? Localized accuracy of IPC for Ipek

27. Can we find optimal designs? Surface allows us to solve for the best values for some variables analytically Other variables have insignificant impact When checked by exhaustive search, for most benchmarks 100% of the optimal designs had the analytically determined values Worst design with those values still had performance that was 97% of optimal

28. How does the technique help architects? Saves time Creates response function automatically Only need to simulate predetermined sample points High accuracy means confidence in predictions Provides insight Which design choices are important What the correct choices are

29. Limitations Time to construct response surface via GPRS rather than with ANNs Hours rather than minutes on this space Still just an up front cost on the order of a single simulation Feedback is an explicit, analytic expression How to determine quality without exhaustive search? Cross validation

30. Questions? Thanks to John Lach, Paul Reynolds, Sally McKee, David Brooks, Karan Singh, Benjamin Lee