1. 1 Server-level Power Control Ming Chen

2. 2 Motivations(1) Clusters of hundreds, even thousands of servers; Occupy one room of a building or even a whole building; Servers racked in cabinets with high density; Cabinets are ordered in rows and columns to occupy a whole room.

3. 3 Motivations(2) From Spring 2005, Data Center User’s Group Conference, The adaptive Data Center: Managing Dynamic Technologies Power and energy consumption have become key concerns in data centers; Solutions: − Peak power management to decrease the cost of cooling systems and power delivery systems; − Power efficient design to improve performance/watts;

4. 4 Outline Power management for CPU Server-level Power Control (paper 1) Formal Control Techniques for Power-Performance Management (paper 2) Comparison between the two papers

5. 5 Why CPU Power Management? The most used actuator in power management; The majority of total power consumption of servers; − More than 60% of the total power consumption. Well-documented interfaces to adjust power scaling. − P-states; − T-states.

6. 6 CPU Power Knob (1)—P-states f p DVFS PowerNOW, SpeedStep, Cool’n’Quiet

7. 7 CPU Power Knob (2)—T-states Duty cycle p

8. 8 Server-level Power Control Charles Lefurgy, Xiaorui Wang and Malcolm Ware IBM Research, Austin University of Tennessee, Knoxville

9. 9 Goal: − Manage the peak power control to avoid unnecessary under-provisioned cooling systems and power delivery systems. Motivations Workload varies a lot very few but worst cases of power consumption Over-provision the cooling system and power delivery system

10. 10 Control Options Open-loop − No measurement of power; − Choose fixed speed for a given power budget; − Based on most power hungry workload; Ad-hoc control − measure power and compare it with the power budget; − raise/lower one level of performance state based on the comparison;

11. 11 Contributions The first paper that manages the peak power of a single server with a closed-loop control system; A feedback controller based on control theory; Detailed derivation and analysis of the stability and accuracy; Empirical results in a physical hardware system; Better application performance than previous methods

12. 12 Platform IBM BladeCenter HS20 blade server with Intel Xeon processors; Power constraint: 250 W No overload of power supply for more than 1 second;

13. 13 System Modeling(1) Power changes immediately as the performance state changes (within 1 ms) Curve fitting Which A to choose?

14. 14 System Modeling(2)

15. 15 Controller Design(1) First-order delta-sigma modulator: − Map a series of discrete throttling levels to the floating-point output of the controller; − For example: 6.2 is discretized as 6, 6, 6, 6, 7, 6, 6, 6, 6, 7; Controller: P controller; Plant:

16. 16 Controller Design(2) Different workloads on the same server have different slope; The same workload on the different servers has different slopes. Slope variatio n Minimal prototype: Real model:

17. 17 Performance Analysis Stability 0 < g < 2 Steady state error Settling time

18. 18 System Architecture Power Monitor − A hardware which can measure the power at 1000 samples/second; − A firmware in the service processor average the power measurements; Controller − Compute the ideal throttling level Actuator − Map the discrete throttling levels to floating-point levels and write the CPU register to throttle the clock.

19. 19 Comparison with Ad-hoc controller(1)

20. 20 Set points are from 180W to 260W with 1W increment; P4MAX is used; The average of three runs is plotted; P controller has a precision of 0.1W; The safe margin of Ad- hoc controller is 6.1 W. Comparison with Ad-hoc controller(2)

21. 21 Comparison of Three Controllers Open-loop set point – P4MAX without violation of power budget P controller set point – Reducing the power budget by 2% measurement error; Ad-hoc controller set point – 6.1W lower than P controller set point.

22. 22 Application Performance

23. 23 Conclusion A control-theoretic peak power management solution for servers is presented; Better control performance and application performance than two baselines; Stability, settling time and zero steady state error are analyzed based on control theory.

24. 24 Critiques Peak power management Vs. performance/watts; Clock throttling + DVFS, what is the solution? A high precision hardware is required which is not available to everyone.

25. 25 Formal Control Techniques for Power-Performance Management Qiang Wu, Philo Juang, Margaret Martonosi, Li- Shiuan Peh, Douglas W.Clark Princeton University

26. 26 Background: MCD Each function block operates with an independent clock; Advantages: − less clock distribution − less clock skew; − less power consumption; − DVFS flexibility Use queue structures between domains for efficiency. INTexec FPexec Ld/Stexec Ifetch/Decode f1f1f1f1 f2f2f2f2 f3f3f3f3 f4f4f4f4

27. 27 Basic Idea Adapt frequency to workload changes; capability > demand: wasted; capability < demand: degraded performance; Queue occupancy – clues about capability and demand; – a feedback signal to control the domain frequency. DVFS controller f q

28. 28 queue q arrival rate frequency f 2 service rate  clock domain demand clock domain frequency f 1 System Modeling(1)

29. 29 queue q arrival rate frequency f 2 service rate  clock domain demand clock domain frequency f 1 System Modeling(1)

30. 30 System Modeling(2) queue q arrival rate frequency f 2 service rate  clock domain demand clock domain frequency f 1 λ t and μ t : independent and stationary random processes; Each control period T includes N sampling period Δ t; q’ k is the controlled variable.

31. 31 System Linearization f is the manipulated variable, but it is nonlinear in the model; It is generally hard to design an effective controller for nonlinear system; Fortunately, the nonlinear part in this system can be separated.

32. 32 Controller Design PI controller – Proportional gain (K_p) – Integral gain (K_i)

33. 33 How aggressively to save energy? – Or preserve performance? A simple lever – q ref position – Increase q ref – more aggressive in saving energy – Decrease q ref – value performance more Software/hardware cooperation – Software – make overall tradeoff decisions – Hardware – implement details of speed adaptation Energy-Performance Tradeoff

34. 34 Experiments(1)– Illustrative Exp Benchmark Epic_Decode: frequency settings queueentries

35. 35 Simulator: SimpleScalar + Wattch power estimation extension + MCD processor extension Benchmarks: 18 benchmarks Experiments Results

36. 36 Extension for CMPs (1) Using task queues; Dependency among parallel application threads; – Parallel sections require all threads to finish before moving on. Two valid assumptions: − The tile with the highest queue occupancy is on the critical path. − The tile on the critical path should run in full speed. What is the solution?

37. 37 Extension for CMPs (2) – Dist_PID q ref the performance lever Each tile estimates q target ; The tiles exchanges their q target ; The tile with the highest q target is identified as the critical path; Other tiles set their q ref as the highest q target.

38. 38 Experiment for Dist_PID Simulator: modified Xtrem (a validated SimpleScalar ARM simulator); Dist_PID has lower EDP than Local_PID thus it has better performance.

39. 39 Conclusion A control-based solution for power-performance tradeoffs of MCD processors and CMPs is presented; An analytical queue model between different MCDs is analyzed; Based on the PI controller for MCDs, a Dist_PID is introduced for CMPs; Simulation results are provided to verify the performance of the controllers.

40. 40 Critiques Effects of λ on the stability or the accuracy of the controller? Simulation results are not convincing enough; Dist_PID only compares with Local_PID. How about other solutions for CMPs? Overhead or delay for exchanging q target in the dist_PID?

41. 41 Comparison between the two papers Server-level Power ControlControl for Power-Performance Control targetCPU Control levelSystem-levelComponent-level ModelCurve-fittingAnalytical ControllerP controllerPI controller GoalsPeak power managementPower-performance tradeoffs ExperimentsPhysical testbedSimulations

42. 42 Thanks!