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
IDA, TU Braunschweig 3
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
IDA, TU Braunschweig 5
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
IDA, TU Braunschweig 6
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
IDA, TU Braunschweig 7
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
IDA, TU Braunschweig 8
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
IDA, TU Braunschweig 9
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
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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
IDA, TU Braunschweig 11
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
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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?
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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
IDA, TU Braunschweig 14
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
IDA, TU Braunschweig 16
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
IDA, TU Braunschweig 17
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 ?
IDA, TU Braunschweig 18
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
IDA, TU Braunschweig 20
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
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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!