System-level, Unified In-band and Out-of-band Dynamic Thermal Control

Dong Li Virginia Tech

Rong Ge Marquette University

Kirk Cameron Virginia Tech

Motivation Hot spots or elevated temperatures in areas of

the data center are quite common Out-of-band techniques (e.g. CPU cooling

fans) are less studied In-band and out-of-band techniques operate

independently without cooperating with each other Challenge 1: enforcing the same user control policy

across diverse physical mechanisms Challenge 2: in needs of a tunable controller

Temperature Characteristics of Parallel Applications

Temperature Characteristics of Parallel Applications Three temperature characteristics

Sudden change and gradual change lead to actual temperature increase or decrease

Jitter lacks sustained increase or decrease following a spike

Design a controller to recognize these types and respond accordingly

History-based Context-aware Temperature Control(Basic idea) Periodically profile temperature and use the

historical information to predict future CPU temperature

Identify the appropriate technique to perform thermal control and balance power and performance for the next interval based on the prediction

History-based Context-aware Temperature Control(Temperature Profiling and Prediction) Use a two-level window to track the changes in

temperature in both long and short time periods

Temperature samples

The level-one temperature window to react to the “sudden”

Average value to reduce jitter

front rearThe level-two temperature window (FIFO) to react to the “gradual”

History-based Context-aware Temperature Control(Temperature Profiling and Prediction) We assume that temperature will change with

the same rate for the next round of sampling

The temperature difference (tL1/L2) is then used to determine the appropriate temperature regulator response

History-based Context-aware Temperature Control(Target Mode Identification) Inputs:

Predicted temperature behavior based on the temperature profile (tL1/L2)

A parameter (Pp) specified by the user that indicates the aggressiveness of the temperature controller

Outputs: Fan speed Frequency setting The controls follow the thermal control policy (Pp)

History-based Context-aware Temperature Control(Target Mode Identification) We maintain a “thermal control array” for each

available thermal control technique on the system

{g1, g2, g3, …, gnp, …, gN}

Each number represents a mode that controls temperature at a degree

Weak Strong

Effectiveness of controlling temperature

History-based Context-aware Temperature Control(Target Mode Identification) To coordinate multiple thermal management

techniques, we fill out the arrays in a unified way

{g1, g2, g3, …, gnp, …, gN}

Weak Strong

Effectiveness of controlling temperature

np is determined by Pp

Filled with the most effective mode gN

Filled with a subset of physically available modes evenly extracted from the full set

History-based Context-aware Temperature Control(Target Mode Identification) np is determined by Pp

PMIN PMAX

1 N

Mapping

np

Pp

History-based Context-aware Temperature Control(Target Mode Identification)

PMIN PMAX

1 N

Mapping

np

Pp

{g1,…, ,…, gN}gnp

A smaller Pp leads to a more aggressive thermal control More array items store the most efficient temperature

mode A small increment in array index can lead to large

increment in cooling effect

History-based Context-aware Temperature Control(Target Mode Identification)

We use the predicted temperature variance (tL1/L2) from the two-level window to identify an index in the thermal control array

{g1,…, gi,…gi+c*t,…, gN}

current mode next mode

TMIN TMAX

1 N

MappingC = (N-1)/(Tmax – Tmin)

Performance Evaluation (Platform)

ADT7467 developing board

Cooling fan on top of processor

Implement a fan driver that dynamically set the fan speed according to processor temperature

Collect temperature samples from digital thermal sensors embedded in the processor

The processor can be scaled among 5 frequencies

Performance Evaluation (Dynamic Fan Control)

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Pp=75

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Pp=75

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Pp=50

Pp=50

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Pp=25

Pp=25

Pp=75

Pp=75

Pp=50

Pp=50 Pp=25

Pp=25

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Our dynamic fan control responds to temperature changes underdifferent control policies (Pp=25 (aggressive), Pp=50(moderate), and Pp=75(weak))

Performance Evaluation (Dynamic Fan Control)

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Sample Points

Tem

pera

ture

(°C

)

Under traditional fan controlUnder our fan controlUnder max fan speed

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Sample Points

PWM

Dut

y C

ycle

Traditional fan controlOur fan controlMax fan speed

Pp = 50; benchmark: bt.B.4 We compare our dynamic fan control method with the

traditional static method and constant fan speed control

Performance Evaluation (Dynamic Fan Control)

In general, larger maximum PWM duty cycle leads to lower temperature

A less powerful fan is able to deliver similar cooling effects as a more powerful fan with our dynamic control

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Sample Points

Tem

pera

ture

(°C

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25% max

50% max

75% max

100% max

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Sample Points

PW

M D

uty

Cyc

le

max 100%max 75%max 50%max 25%

Performance Evaluation (Temperature Aware DVFS Control)

Benchmark: LU.B.4; coupled with traditional static fan control; Pp=50 Our DVFS control scales down frequency only when average

temperature is stabilized Our DVFS control scales up frequency to its original value once the

temperature is consistently below the threshold so as to avoid performance loss

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Te

mp

era

ture

(°C

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Trigger Temperature = 51 °C

Freq change:2.4GHz --> 2.2GHz

Freq change:2.2GHz --> 2.4GHz

Performance Evaluation (Temperature Aware DVFS Control)

Our DVFS control performs better than CPUSPEED in terms power-saving and performance

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Te

mp

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ture

(°C

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CPUspeed

tDVFS

Freq changes:2.4GHz -->2.2GHz

Freq changes:2.2GHz --> 2.0GHz

CPUSPEED tDVFS

Max allowedPWM duty cycle

75% 50% 25% 75% 50% 25%

# freq changes 101 122 139 2 2 3

Execution Time (s) 219 222 223 219 233 234

Ave Power(Watt) 99.78 99.30 100.80 97.93 94.19 92.78

Power-Delay Product (Watt*s)

21852.78 22044.21 22478.64 21447.27 21946.03 21710.32

Performance Evaluation (Dynamic Hybrid Fan and DVFS Control)

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sample points

Te

mp

era

ture

(°C

)

Pp = 75Pp = 50Pp = 25

3 14 2

Our method effectively unifies different thermal control techniques and reacts to different user control policies with minimum performance impact

Conclusion We classify thermal characteristics of parallel

applications and use a two-level temperature window to make our controller more effective

We introduce a simple parameter (Pp) to allow the user to specify the aggressiveness of in-band and out-of-band techniques for thermal reductions

We integrate an out-of-band method (fan control) and an in-band method (DVFS)

We explore an efficient fan control method

Thank You