Performance characterization and sensitivity analysis

Performance characterization and Performance characterization and

sensitivity analysissensitivity analysis

Razvan Racu

WiMi Meeting - 22.05.2008

IDA, TU Braunschweig 2

OutlineOutline

Razvan @ IDA

Sensitivity analysis

Motivation

One-dimensional

Multi-dimensional

Extensions

Timing anomalies

Power optimization


Razvan @ IDARazvan @ IDA

2000 - 2002 HiWi (SymTA/P)

static timing analysis for tasks

cache simulator

2000 - 2001 WiMi (SPI)

2001 – today WiMi (SymTA/S)

respose time analysis

sensitivity analysis

Sensitivity analysisSensitivity analysis


MotivationMotivation

Modifications of design properties

During the design process Refinement of early design data estimations Refinement and changes of specification Exchange of platform components: replace CPU or memory

type

In the product lifecycle Product updates (HW, firmware and SW) Integration of new components or subsystems Change in the environment: applications (smartphone),

technical system (motor speed)

In the field Unplanned environment situations (resilience)

Such changes introduce uncertainties and increase design risk


Domino effects due to parameter changesDomino effects due to parameter changes

gateway

ECU1

diagnosis

CAN1 CAN2

FlexRay

ECU2

ECU3

ECU4

ECU5

ECU6

ECU8

ECU7

T1

T2T1

T1

T1 T2

T2T1

T3 T4

T1 T2

T2

T1 T3

T1

T2overload loss

loss

loss

loss

loss

overload


Sensitivity analysisSensitivity analysis

Sensitivity analysis identifies limits of feasible design

How far can system parameters be changed before the system fails?

Evaluates design risk linked with a specific component

Helps to controls parameter changes

Captures „domino“- effects

Applications

Metric for design robustness

Assistance for system dimensioning/configuration


System properties and metricsSystem properties and metrics

System properties

Task execution times (BCET, WCET)

Communication volume

Resource speed

Event model parameters

Buffer capacity

Metrics

End-to-end latencies

Resource utilization

Task response times

Output timing parameters

Activation backlogs


Sensitivity analysis frameworkSensitivity analysis framework

Based on SymTA/S analysis engine

Formally derived search space boundaries

either based on load conditions …

… or intrinsic relations between system properties

Binary search technique

transparent with respect to scheduling algorithms and application structure

optimal minimum number of search steps

bidirectional search space feasible infeasible infeasbile feasible

applicable only on monotonic search spaces if non-monotonic behavior derive monotonic sub-spaces


Performance characterizationPerformance characterization

Determines the characteristics of the performance metrics

monotonicity

continuity

Requires a good description of the performance metrics

best-case and worst-case response times

output timing parameters

in general applicable only on local components


Why multi-dimensional sensitivity analysis?Why multi-dimensional sensitivity analysis?

gateway

ECU1

diagnosis

CAN1 CAN2

FlexRay

ECU2

ECU3

ECU4

ECU5

ECU6

ECU8

ECU7

T1

T2T1

T1

T1 T2

T2T1

T3 T4

T1 T2

T2

T1

T3

T1

T2

T3

T4

Integration of new applications


Pseudo multi-dimensional sensitivity analysisPseudo multi-dimensional sensitivity analysis

Considers system parameters with common properties

Resource speed: scales the WCET of all tasks by the same factor

Functional paths: the execution and the communication depends on the input data volume

Can be reduced to one-dimensional case

What about totally independent parameters?


Approach 1Approach 1

Determines exact values on the sensitivity front

Border between feasible and unfeasible system

configurations

Combines one-dimensional sensitivity analysis with a

divide and conquer-like algorithm

Check equidistant values within search space

(divide)

Smart step (conquer): exploits the monotonic

behavior of the analyzed parameters to reduce

algorithm complexity


Smart stepSmart step

Identify the intervals with equal slack values at margins

Draw the sensitivity front based on the monotonic properties of the parameters

6

6,5

7

7,5

8

8,5

9

9,5

10

10,5

11

9 11 13 15 17 19 21 23 25 27

WCET(T2)

WC

ET(T

1)

6,5

10

approximatedexact

1D

searc

h s

pace

Timing anomaliesTiming anomalies


Response time analysisResponse time analysis

Tindell (1994), Palencia (1998), Henia(2004)

tasks are grouped in transactions

account for time correlations between transaction tasks

tighter response time bounds

200

220

240

260

280

300

0 20 40 60 80 100 120 140 160 180 200

offset between T1 and T2

RT

3


Problem definition Problem definition

Current approaches

Given a fixed activation offset, what are the

response times

Timing anomaly analysis

What happen when the activation offset changes ?

Where are the anomalies ?


Scheduling anomaliesScheduling anomalies

Activation offset determined by the execution times of

the transaction tasks

Variation of task properties during design process

performance bottlenecks

additional performance reserves

Power optimizationPower optimization


Dynamic powerDynamic power

Reduce power by reducing voltage and frequency

Reduce processor speed Increase execution times

Power reduction strategies

Dynamic voltage scaling (DVFS)

Multiple supply voltages (MV)

fVCP ddLdynamic2


Our approach to power optimizationOur approach to power optimization

Two power optimization approaches

Given a set of tasks and their mapping to resources

Task level: determine the voltage/speed of each task that minimizes power (DVFS)

Resource level: determine the static voltage/speed of each resource that minimizes power (MV)

Two optimization algorithms for each approach

Heuristic: use sensitivity analysis of timing properties

Stochastic: use evolutionary algorithms

Thank you!Thank you!

Performance characterization and sensitivity analysis

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