1. Green: A Framework for Supporting Energy-Conscious Programming using Controlled Approximation Woongki Baek Stanford University Trishul M. Chilimbi Microsoft Research PLDI 2010

2. General Problem  Tradeoff between Quality of Service (QoS) and Performance + Energy Consumption  Why is it critical?  Datacenters: Amazon, Google, Microsoft  Existence of acceptable domains: Machine Learning, Image/Video Processing  Programmers do this anyway, but in an ad-hoc manner and no QoS guarantees  Green Framework was built to address these issues

3. Green Framework Overview  Static and dynamic calibration  QoS Model  Language Extension

4. Functionality  Input provided by programmer:  QoS loss (maximum acceptable) i.e. 2%  QoS computation function through language extension  Optionally:  approximate version(s) of a function or a loop  calibration mechanism  Output by Green system:  New version of a program that is tuned to be more efficient (both performance and power)  QoS guarantees to be in the acceptable range  Dynamic re-calibration (adaptation) if needed at run- time

5. Overview  General Problem and Functionality  System Design  Green Implementation  Benchmarks  Evaluation  Discussion

6. Green Framework Design  QoS Service Level Agreement (SLA)  QoS_Compute  QoS_Approx and QoS-ReCalibrate

7. Green Mechanisms

8. Loop Approximation To be replaced

9. QoS_Compute Used in the Calibration Phase

10. QoS_Lp_Approx

11. QoS Model for Loops  Calibration data goes to MATLAB program  Automatically selects appropriate approximation level  Provides 2 interfaces:

12. Function Approximation

13. Approximation Modeling  For each function and loop individually  Then extensive search space exploration to combine them and still fulfill QoS SLA  Global recalibration based on QoS_Loss/Performance_Gain  Exponential backoff scheme to avoid non- linear effects if any

14. Experiments Benchmarks:  Bing Search  Graphics: 252.eon (SPEC2000)  Machine Learning: Cluster GA  Signal Processing: Discrete Fourier Transform  Finance: Blackscholes (PARSEC)

15. Results with Bing Search - Performance

16. Results with Bing Search - QoS

17. Results with 252.eon - Performance

18. Results with 252.eon - QoS

19. Results with 252.eon – Model Sensitivity

20. Results with DFT – Performance And Energy

21. Results with DFT – QoS

22. Discussion Advantages:  Performance and power efficiency improvements  Design flexibility  Automatic QoS modeling  Fine granularity: function and loop based

23. Discussion Disadvantages:  Limited scope of application: sin, cos, log, exp  Complexity for programmer: QoS_Compute, approx_loop extensions  Semi-automatical  Limited QoS approach  Only numerical data as input, no structures in QoS modelling