a case for coordinating accuracy-aware applications with power-aware systems

Department of Computer Science

A Case for Coordinating Accuracy-aware Applications with Power-aware

Systems

Henry Hoffmannhankhoffmann@cs.uchicago.edu

Balancing Goals With Programmability

2

Performance Programmability

Balancing Goals With Programmability

3

Performance

ProgrammabilityPower

Lo Hi

Power

Balancing Goals With Programmability

4

Performance

Programmability

Power

Accuracy

Lo Hi

Power

Partial Solutions

5

Per

form

ance

Per

form

ance

Accuracy Power

Accuracy-aware Applications(e.g. DynamicKnobs, Quality-of-Service Profiling, EnerJ, Petabricks, Eon, Green)

Power-aware Systems(e.g. Maybe too many to list PTRADE, METE, ControlWare,

Agilos, Swift)

Each is provably optimal individually. What happens when they interact?What guarantees can we provide?

Why Coordinate the Two?

• Avoid bad behavior

• Provide better outcomes than accuracy adaptation alone

• Add new capability to react to environmental changes 6

Why Does Bad Behavior Arise?

7

Pow

er (

W)

Acc

urac

y Lo

ss (

%)

Configuration

x264 Video Encoder

PowerAccuracy Loss

Accuracy-aware application prefers perfect accuracy, at high power

Power-aware system prefers saving power, at high accuracy loss

Example Of Bad Behavior

• Run x264 (video encoder) on real Linux x86 system with

• Problem is performance becomes a non-linear (and non-convex) function of application and system

8

Why Run Both?

9

Combining accuracy- and power-awareness leads to:

1) Better energy efficiency for same accuracy or

2) Better accuracy for same energy efficiency

How Can We Solve This Problem? [ECRTS 2014]

• Split into multiple dimensions: lead and subordinate• Control lead• Dynamically construct controller for subordinate• Subordinate controller approximates linearity

10

PowerController

PowerTranslator

Application &System

-

PerformanceController

PerformanceTranslator-

gp(t)

gs(t)

ep(t)

es(t)

up(t)

us((t)ks(t)

kp(t)

fs(t)

fp(t)

Avoids Bad Behavior

11

Reacts to Application Resource Needs

• Video with three distinct scenes• Coordination reacts to phases while:

– Maintaining real-time performance– Reducing power consumption– Providing higher accuracy

12

Reacting to Changing Goals

13

0 50 100 150 200 2500

0.5

1

1.5

2

Norm

alize

d

Perf

orm

an

ce

0 50 100 150 200 2500

1

2

3

4

Norm

alize

d

Pow

er

0 50 100 150 200 2500

1

2

3

4

Accu

racy

Loss

Real-time, Low-power Real-time, High Accuracy

Support Goals and Goal Changes in Any Two of Three Dimensions

14

Conclusions

• Coordination of adaptive applications and systems is necessary

• Possible with cascading control systems

• Cascading control:– Avoids bad behavior– Provides better outcomes– Provides greater responsiveness to

fluctuations in workload and changes in goals

15

Observe Act

Decide

Questions?

16

Observe Act

Decide

Backup

17

Selecting the lead dimension

• The lead dimension becomes the one with fewer “knobs” (options)

• The subordinate dimension has more options

• Intuition:– After controlling lead, there will still be knobs affecting

subordinate 18

Controlling the Lead Dimension

• 5-7 compute a control action• 8-19 ensure that:

– Goal met in lead dimension – Affects on subordinate dimension are optimal

• Intuition:– Anything we do in the lead should have smallest affect on subordinate

19

Constructing a Subordinate Controller

• Approximate affect of lead dimension on subordinate (20)

• Construct controller based on this approximation (21)

• Intuition:– Approximate non-linear interaction by constructing tangent to

true curve– Converges to true behavior (analogous to derivative in

calculus)

20

Controlling the Subordinate Dimension

• 22-33 ensure control for subordinate does not affect lead

• Also, control should be optimal in any remaining dimensions

• Intuition:– Use subordinate actions that do not affect lead 21