Dynamic Power Management (DPM)

Transcription

1 Dynamic Power Management (DPM) 1

2 What is DPM? A design methodology aiming at controlling performance and power levels of digital circuits and systems with the goal of extending the autonomous operation time of battery-powered systems, providing graceful performance degradation when supple energy is limited, and adapting power dissipation to satisfy environmental constraints 2

3 Problem definition Most of the systems don t need to run at peak performance all the time DPM reduces power consumption by dynamically adjusting performance levels System can view as a collection of interacting resources 3

4 DPM system DPM is a control problem Policy is a control law The Power Manager unit (PM)is a controller which collects observations from the system and issues an appropriate command 4

5 Application Domain Portable systems e.g. Palm, Pager, GPS, Cellular Phone Operational time Non-Mobile System e.g. Server, PCs Environmental impact 5

6 Power Management System Time varying workload - interactive system - Communication devices Multiple states of operation - Trade off power for performance - State Transition can be controlled 6

7 DPM s Cost State transition cost - Transition Power - Transition Time No state transition cost - Policy is trivial. Shut down when idle. Turn on otherwise. Has State transition cost - Shut down when idle period is long enough to amortize the transition cost 7

8 Model of Power Management System What type and how much information should be exchanged between a PM and system components in order to implement effective policies? 8

9 Power Manageable Component (PMC) Many operation modes give a fine control on how to operate a PMC in such a way that power waste is minimized and performance is perfectly calibrated on the task PMC can be managed internally of externally, according to the physical location of the implementation of the corresponding policy 9

10 Power Managed System Self-managed component appear as non-controllable to the PM The power consumption of all noncontrollable components makes up a base line power consumption that can t be reduced by PM PM can be either hardware block or software or hybrid hardwaresoftware implementations 10

11 Power Management System Parameter: T ms Minimum sleeping time (T ms ) Break even time(t BE ) 11

12 Power Management System Parameter: T ms Energy during sleep state <= Energy during normal operation. Use this constrain to find T ms E sd +E wu +P s T ms = P w (T ms +T sd +T wu ) T ms = E sd +E wu -P w (T sd +T wu ) P w - P s 12

13 Power Management System Parameter: T BE,T bs, T idle T BE = T ms + T sd + T wu T BE = E sd +E wu -P s (T sd +T wu ) P w - P s T bs = Time before shut down Shutdown command can save power iff T idle > T bs + T BE 13

14 Power Management System Parameter: P ideal P ideal = Power cause by the optimum policy (Controlling the transitions between state on and off consisting of shutdown the component at the beginning of all idle period longer than T BE and wake up right in time to serve upcoming requests with no delay) 14

15 Power Management System Parameter : P save P save is the different between P ideal the power consumption of the system when in active state The larger T BE with respect to the average idle time the smaller P save 15

16 Predictions Over prediction performance penalty Under prediction power wasted but no performance penalty Ideal predictor totally safe (i.e. never makes over-prediction, and totally efficient (i.e. never makes under-prediction) 16

17 Synthesis of Power Management Scheme Policies with provable optimality properties Predictive technique - Fixed timeout (Static Technique) - Adaptive timeout Technique - Others 17

18 Synthesis of Power Management Scheme Stochastic technique - Static Technique: Discrete-time Markov (DM) decision process*, Continuous-time Markov process (CM), Semi-Markov approach (SM) - Adaptive technique: Adaptive Learning Tree * Extend to handle non-station requests by sliding window (SW) 18

19 Predictive Technique Exploiting the correlation between the past history of the workload and its near future in order to make reliable predictions about future events 19

20 Predictive Static Technique (Fixed Timeout) When an idle period begins, the timer is started with duration T TO. If after T TO the system still idle, then the PM forces the transition to off state. The system remains in off state until it receives a request from the environment that signals the end of the idle period 20

21 Fixed Timeout Assume that Prob(T idle > T be + T TO T idle > T TO ) = 1 Critical design is the choice of the timeout value Advantage: General (Applicability slightly depends on the workload), safety can be improved simply by increasing the timeout value 21

22 Fixed Timeout Trade-off efficiency for safety If safety has a high non-smooth instance-dependent behavior, it s difficult to choose optimal timeout value T TO = T BE lead to energy consumption that is at worse twice the energy consumed by an ideal policy [1] 22

23 Fixed Timeout Limitations: waste a sizeable amount of power waiting for the timeout to expire (can be addressed by predictive shut-down policies[2]), always pay a performance penalty upon wakeup (can be addressed by predictive wakeup[3]) 23

24 Predictive Adaptive Technique ATO1: Adjust T TO by considering the ratio of T idle [i-1]/t wu. When ratio small, T TO increase and vise versa[4]. ATO2: Updating T TO asymmetrically[5]. ATO3: Adjust T TO according to T busy [i]. If T busy [i] is small, T TO decrease and vise versa[6]. 24

25 Predictive Technique: Others L-shape: When request pattern form an L-shape, the device should be shut down after a short busy period[7]. Exponential Average: Using exponential average to predict the length of the current idle period[8]. 25

26 Disadvantage of Predictive Technique assume deterministic response and transition times for system Predictive algorithm are base on 2 state system model Predictive algorithm is geared toward power minimization and can t finely control performance penalty 26

27 Disadvantage of Predictive Technique Predictive algorithm is heuristic and can only be optimizing by gauged through comparative simulation. And parameter tuning can be very hard if many parameter are involves. 27

28 Stochastic Technique Stochastic control formulates policy optimization as an optimization problem under uncertainty rather than trying to eliminate uncertainty by prediction. Model both system and workload as stochastic processes 28

29 Stochastic Technique Stochastic control use Markov model which consist of SR (model the arrival of service requests for the system), SP (model the power states of the system), PM, Cost matrices (associate power and performances values with each system state-command pair) 29

30 Advantage of Stochastic Technique over Predictive Technique Captured the global view of the system which allow the designer to search for a global optimum that possible exploits multiple inactive states of multiple interacting resources 30

31 Advantage of Stochastic Technique over Predictive Technique Enable the exact solution of the performance-constrained power optimization problem Exploits the strength and optimality of randomized policies 31

32 Draw back of Stochastic Technique The performance and power obtained by a policy are expected values, there s no guarantee that the results will be optimum for a specific workload instance Policy optimization requires a Markov model for SP and SR which sometime is not applicable 32

33 Draw back of Stochastic Technique Policy implement not straightforward Markov model for SR and SP can be just an approximation and if the model is not accurate then the optimal policies are just approximate solution 33

34 Draw back of Stochastic Technique For non-stationary non-markovian workload, policy optimization by stochastic control is not guaranteed to provide optimum result 34

35 Stochastic Technique System model component Service Provider (SP) Service Requester (SR) Queue (Q) Cost Metric (c) and Service rate (b) Power manager (PM) - Decision - Policy 35

36 Stochastic Technique System component are modeled as Markov chain 36

37 Service Provider (SP) SP state, Sp = {1,2,,Sp} 37

38 Service Provider (SP) tn \t n+1 on off P SP (s_on) = on 1 0 off tn \t n+1 on off P SP (s_off) = on off

39 Service Requester (SR) SR state, Sr = {0,1,,Sr-1} 39

40 Service Requester (SR) tn \t n P SR =

41 Queue (Q) State set,q ={q i s.t. i = 0,1,,Q-1}. Queue length = Q-1 41

42 Queue (Q) Queue is in state q i when i requests are waiting to be serviced. Queue is bonded SP control how fast the queue is emptied. SR controls how fast the queue is filled 42

43 Queue (Q) FIFO queue with P sq (S p, S r ) tn \t n PS SQ (on,0) = tn \t n PS SQ (off,0) =

44 Queue (Q) FIFO queue with P sq (S p, S r ) tn \t n PS SQ (on,1) = tn \t n PS SQ (off,1) =

45 Power Consumption rate (c) Each power state has a specific power consumption rate, c associate with it. C is a function both of the state and the command performed on the state: c(sp,a) s_on s_off c(sp,a) = on 3 4 off

46 Service rate (b) s_on s_off b(sp,a) = on off 0 0 SP active only when it s in the on state and it s not being switch off. 46

47 Formulation of the policy optimization problem Formally describe the behavior of the PM Find the particular behavior that optimally reduces power dissipation under performance or power constraints Provide formal definition for power and performance cost metric 47

48 Power management Control procedure that issues a command a A to SP every time period t n Communicates with SP and attempts to set its state at the beginning of each period, by issuing commands chosen among a finite set A. 48

49 Power management Commands contain all proper specifications and collect all relevant information (by observing SR and PM) needed for implementing a power management policy. Power consumption of PM is small and can be negligible. 49

50 Power management States of the system, s, composed of the SP, the SR and the queue state. s = (s r,s p,s q ) s is a Markov chain with s r x s p x s q states whose transition matrix P(a) depend on a command a issued to the SP by the PM System is fully describe by a set of transition matrices, one for each command 50

51 Decision At the beginning of time period n, PM observes the history, H n of the system (i.e. the sequence of states and commands up to n-1) and controls SP by taking decision δn Deterministic decision Randomized decision 51

52 Policy Stationary policy Randomized stationary policy 52

53 Cost Metric Function of both decision and state i.e. the decision taken when in state s Expected power consumption level c(s p,δa) per unit time Performance penalty per unit time d(s q ): depend on queue length( number of job in a queue). Easiest performance penalty = s q 53

54 Policy Optimization Policy optimization (PO): search the space of all possible policies to find the one that minimizes a cost metric (Power and performance) PO targets the optimization of one cost metric while using the second as constraint 54

55 Stochastic Static Technique Discrete-time Markov (DM) decision process[9] Discrete time t = 1,2, Modeling choice of time granularity SR and SP modeled by Markov chains with finite state space PM can issue a finite number of command 55

56 Stochastic Static Technique sliding window (SW) DMP[10] User request prediction - Single window approach - Multiple window approach Policy Table: Dimension corresponding to # of SR states. Choosing decision using interpolation 56

57 Stochastic Static Technique Continuous-time Markov process (CM)[11] PM change power states upon event occurrences Make decision as soon as certain events happen. 57

58 Stochastic Static Technique Semi-Markov approach (SM)[12] Time-indexed Semi-Markov decision process model Markovian randomized stationary policies 58

59 Stochastic Static technique SM and CM decision are made at each event occurrence (Event-driven PM)instead of at each discrete time interval (Clock-driven PM)as in DM. Event-driven PM make decision only in response to the changes in the workload and in the state of operation of the system without creating additional activity in each clock cycle when the system is idle. 59

60 Stochastic Static technique Semi-Markov model is more general than the Markov model as it allows general inter-state time distributions instead of requiring geometric (DM) or exponential (CM) distribution. As a result, it can accurately represent a larger class of system 60

61 Stochastic Adaptive Technique Adaptive Learning Tree[13] Adaptive Technique try to over come the problem of non-stationary workload. Idle Period Clustering Decision Learning 61

62 Stochastic Adaptive Technique Enhance scheme for PM of Adaptive learning tree technique Wake-up and miss correction Prediction filter 62

63 Implementation on DPM Clock Gating Supply Shutdown Multiple and variable power supply 63

64 Clock Gating Small overhead in term of additional circuit and often zero performance overheads since the component can transition from an idle to an active state in one (or few) cycle 64

65 Clock Gating Power can be reduce even further by stopping not only clock distribution, but also clock generation (i.e. stop the master clock PLL or internal oscillator). This choice implies nonnegligible shutdown and restart delay and it s generally not automated. 65

66 Supply Shut down Power off the idle unit. This requires controllable switches on the component supply line. Wide applicability to all kind of electronic components (i.e. digital and analog units, sensor and transducers), electro-optical and electro-mechanical system component. 66

67 Supply Shut down Component s operation must be reinitialize and hence high wake-up recovery time. Mechanical moving part like HDD state transition involve in accelerating and decelerating moving part and hence decrease the expect life time of the component which can be seen as another cost associate with state transition. 67

68 Clock Gating Vs Supply Shut down Construct an idleness-detecting circuit, which is small, and consume little power and accurate (i.e. able to stop the clock whenever the component is idle) is challenging Design gated-clock distribution circuitry that introduce minimum routing overhead and keep clock skew low is challenging 68

69 Clock Gating Vs Supply Shut down Clock gating doesn t eliminate power dissipation. And leakage current dissipation power still present even when all clock are halted. 69

70 Multiple and variable power supply Component that are not idle but performance requirements varies with time. Slowdown of non-critical components by lowering the voltage supply 70

71 Multiple and variable power supply Enable dynamic adjustment of power supply voltage during system operation Clock frequency tracks the speed changes caused by dynamic voltage supply adjustments 71

72 System Level Implementation DPM scheme at system level can coexist with local PM of the component PM can be hardwired or micro programmed controller 72

73 System Level Implementation Policies base on timeout are implemented by timers while policy base on stochastic control can be implemented by look-up table or by sequential circuits. Randomized policy require the use of pseudorandom number generator that can be implemented by LFSR 73

74 System Level Implementation Implement OS-based power management (OSPM) is hardware/software co-design problem because the hardware resources need to be interfaced with the OS-based software PM and both hardware and software application programs need to be designed so that they cooperate with OSPM. 74

75 System Level PM standard OnNow supports the implementation of OSPM and targets the design of personal computers with improved usability through innovative OS design ACPI simplifies the co-design of OSPM by providing an interface standard to control system resources 75

76 ACPI OS-independent power management and configuration standard. Defines interfaces between OS software and hardware. Doesn t provide management control scheme Applicable to PCs only 76

77 ACPI Module of the OS implements the power management policies. The power management module interact with the hardware through system calls. The kernel interact with the hardware using device driver 77

78 ACPI 78

79 ACPI ACPI driver OS-specific Maps the kernel request to ACPI commands ACPI response to kernel using interrupt. 79

80 ACPI Hardware Platform Hardware resources (devices) e.g. bus controller, modem CPU : need to be active for the OS and ACPI interface layer to run Chipset (core logic): Motherboard logic that controls the most basic hardware functionalities and interfaces the CPU with all other devices. 80

81 State definition of ACPI G3: Mechanical off state G2: Soft off state G1: Sleeping state G0: Working state Legacy state S1-S4: Sleeping state within G1. D0-D3 & C0-C3: Device and processor states 81

82 State definition of ACPI 82

83 OnNow PC turn-on delay is negligible The operating system and applications work together intelligently to operate the PC to deliver effective power management in accordance with the user s current needs and expectations. 83

84 OnNow Relies on the ACPI infrastructure to interface the software to the hardware components to be managed. Resources participate in DPM by response to OS commands. 84

85 OnNow States global states -working -sleep -off Device Power state, D0-D3 85

86 OnNow 86

87 Reference [1] A. Karlin, M. Manasse, L. McGeoch and S. Owicki, Competitive Randomized Algorithms for Non uniform Problems, Algorithnmica, Vol. 11 No. 6, pp , June 1994 [2] M. Srivastava, A. chandrakasan, R. Brodersen, Predictive System Shutdown and Other Architectural Techniques for Energy Efficient Programmable computation, IEEE Transactions on VLSI system, vol. 4, no. 1, pp.42-55, March 1996 [3] C.-H Hwang and A. Wu, A predictive System Shutdown Method for energy Saving of Event-Driven Computation, International Conference on Computer-Aided Design, pp28-32, Nov [4]F. Douglis, P. Krishnan, and B. Bershad, Adaptive Disk Spin-down Policies for Mobile Computers, Computing Systems, volume 8, pp ,

88 Reference [5] R. Golding, P. Bosch, and J. Wikes, Idleness is not Sloth, USENIX Winter Conference, pp , 1995 [6]Y. -H. Lu, T. Simunic, and G. D. Micheli, Adaptive hard disk Power Management on Personal Computers, Great Lakes Symposium on VLSI, pp 50-53, 1999 [7] M. B. Srivatava, A. P. Chandrakasan, and R. W. Brodersen, Predictive System Shutdown and Other Architecture Techniques for Energy Efficient Programmable Computation, IEEE Transactions on VLSI Systems, pp 42-55, March 1996 [8] C. H Hwang and A. C. Wu, A Predictive System Shutdown Method for Energy Saving of Event-Driven Computation, International Conference in Computer- Aided Design, pp 28-32,

89 Reference [9] G. A. paleologo, L. Benini, A. Bogliolo, G. De Micheli, Policy Optimization for Dynamic Power Management, [10] Eui-Young Chung, L. Benini, A. Bogliolo, G. De Micheli, Dynamic power Management for non-stationary service requests, [11] Q. Qiu and M. Pedram, Dynamic Power Management Based on Continuous-Time Markov Decision Processes, Design Automation Conference, pp , 1999 [12] Tajana Simunic, Luca Benini, Giovanni De Micheli, Event Driven PM of Portable Systems, 89

90 Reference [13] Eui-Young Chung, Luca Benini, Giovanni De Micheli, Dynamic Power Management Using Adaptive Learning Tree, [14] Yung-Hsiang Lu, Eui-Young Chung, Tajana Simunic, Luca Benini, Giovanni De Micheli, Quantitative Comparison of Power Management Algorithm, [15] L. Benini, A. Bogliolo, G. De Micheli, A Survey of Design Techniques for System-level Dynamic Power Management, [16] G. De Micheli, L. Benini, A. Bogliolo, Dynamic Power Management of Electronic Systems, 90

91 Reference [17] [18] 91