Performance characterization and sensitivity analysis

1. Performance characterization and sensitivity analysis Razvan Racu WiMi Meeting - 22.05.2008

2. Outline • Razvan @ IDA • Sensitivity analysis • Motivation • One-dimensional • Multi-dimensional • Extensions • Timing anomalies • Power optimization IDA, TU Braunschweig

3. Razvan @ 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 IDA, TU Braunschweig

4. Sensitivity analysis

5. Motivation • 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 changes CAN1 CAN2 loss ECU1 ECU4 T1 T1 T2 ECU2 ECU5 loss T1 T1 T2 loss ECU6 overload ECU3 T1 T1 T2 loss loss T2 T3 T4 gateway diagnosis overload ECU8 T2 T1 T3 ECU7 T1 T2 FlexRay IDA, TU Braunschweig

7. Sensitivity 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 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 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 IDA, TU Braunschweig

10. Performance 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? CAN1 CAN2 ECU1 ECU4 T1 T1 T2 ECU2 ECU5 T1 T1 T2 ECU6 ECU3 T1 T1 T2 T2 T3 T4 gateway diagnosis ECU8 T2 T3 T1 T4 ECU7 T1 T2 Integration of new applications FlexRay T3 IDA, TU Braunschweig

12. Pseudo 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? IDA, TU Braunschweig

13. Approach 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): exploitsthe monotonic behavior of the analyzed parameters to reduce algorithm complexity IDA, TU Braunschweig

14. Smart step approximated exact • Identify the intervals with equal slack values at margins • Draw the sensitivity front based on the monotonic properties of the parameters WCET(T1) 1D search space 6,5 10 WCET(T2) IDA, TU Braunschweig

15. Timing anomalies

16. Response time analysis • Tindell (1994), Palencia (1998), Henia(2004) • tasks are grouped in transactions • account for time correlations between transaction tasks • tighter response time bounds IDA, TU Braunschweig

17. 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 anomalies • Activation offset determined by the execution times of the transaction tasks • Variation of task properties during design process  performance bottlenecks  additional performance reserves IDA, TU Braunschweig

19. Power optimization

20. Dynamic 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) IDA, TU Braunschweig

21. Our 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 IDA, TU Braunschweig

22. Thank you!