1. Soner Yaldiz, Alper Demir, Serdar Tasiran Koç University, Istanbul, Turkey Paolo Ienne, Yusuf Leblebici Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland Characterizing and Exploiting Task-Load Variability and Correlation for Energy Management in Multi-Core Systems ESTIMedia 2005

2. 2 Multi-Core Soft Real-Time Systems processors Chip-level multiprocessing for massive performance –Energy management problem Real-time multimedia applications –Audio, video processing Soft real-time systems –Tolerance to deadline misses tt + T DEADLINE time start end T2 T4T3 T1 task graph MPEG2 video frames

3. ESTIMedia 2005 3 Variability and Correlation Capture by Stochastic Models Exploit for Energy Management – –Dynamic Voltage Scaling (DVS) Time Voltage V1 deadline V2 workload Task i variability probability Task 2 workload Task 1 workload positive correlation This work: First approach to consider variability and correlations for multiprocessor energy management

4. ESTIMedia 2005 4 Application composed of two tasks on a single processor Motivating Example start end T2T1 T DEADLINE = 2 sec Task loads low (2) or high (10) with equal probability Processor Operating Modes – –Slow Mode -> 6 instructions-per-second – –Fast Mode -> 10 instructions-per-second 2 10 instructions T 1,T 2 50% probability

5. ESTIMedia 2005 5 start end T2T1 T DEADLINE = 2 sec 2 10 instructions T 1,T 2 50% probability T1 T2 210 2 25% 10 25% T1 T2 210 2 50%0 10 050% T1 T2 210 2 050% 10 50%0 Probabilities for task load combinations: IndependentPositively Correlated Negatively Correlated Task Load Combinations

6. ESTIMedia 2005 6 T1 T2 210 2 25% 10 25% Motivating Example T1 T2 210 2 50%0 10 050% T1 T2 210 2 050% 10 50%0 Independent Positively Correlated Negatively Correlated Slow mode -> 12 instructions in 2 sec Misses desired performance 0.750.50 never happens ! Fast mode -> 20 instructions in 2 sec Suboptimal energy 1.0 Application – –2 tasks Processor modes – –Slow 6 inst/sec – –Fast 10 inst/sec Deadline – –2 sec Target75% AssumptionIndependent RealityPositive Correlation Target100% AssumptionIndependent RealityNegative Correlation

7. ESTIMedia 2005 7 Stochastic Modeling Energy Management Scheme –OFFLINE Optimization –ONLINE Adjustments Experimental Results Conclusions OUTLINE

8. ESTIMedia 2005 8 Stochastic Modeling Flow Computational Demand (CD) of a task –Number of CPU cycles for execution Demands are represented by dist –Quantized for manageability dist is obtained from a set of traces Demand of tasks constitutes an ‘observation’ –(T1,T2) = ( 5, 5 ) observed 3 out of 8. –dist ( 5,5 ) = 3/8 OBSERVATIONS Task1Task2 1210 255 325 4 2 555 62 725 855 start end T2T1 T2 2510 2 2/8 5 3/8 10 1/8 dist

9. ESTIMedia 2005 9 MPEG2 video decoding –Widely-used and computationally intensive Slice-based task decomposition(Olukotun et.al,1998) –VLD ( Variable-length decoding) –MC ( Motion compensation ) Case Study: MPEG2 VLD0, MC0 VLD1, MC1 VLD2, MC2...... Experimental Data: – –10 movie segments – –19 slices, 38 tasks – –24 frames-per-second – –~ 14000 frames per movie Task Assignment Processor Precedence Data Precedence slice0 slice1slice2

10. ESTIMedia 2005 10 Variability of MPEG2 Task Loads aggregate one movie aggregate 1- Similarity Traning set predicts workload for others 2- Long Tails Worst-Case causes overdesign one movie

11. ESTIMedia 2005 11 Correlation among MPEG2 Task Loads High Correlation aggregate statistics one movie Slice 9 Slice 14 Slice 18 Slice 0 Slice 5...

12. ESTIMedia 2005 12 Critical Path Summation of worst-case task loads : 64 million cycles Observed worst-case total load: 28 million cycles Ignoring correlations lead to far from optimal

13. ESTIMedia 2005 13 Stochastic Modeling Energy Management Scheme –OFFLINE Optimization –ONLINE Adjustments Experimental Results Conclusions OUTLINE

14. ESTIMedia 2005 14 OFFLINE: Optimization Formulation Nonlinear constrained optimization problem with 38 variables – –One voltage per task Stochastic programming formulation – –Based on stochastic application model Optimized voltages stored for run-time look-up Each task i has fixed voltage V i for all periods GOAL: Determine optimal V i ’s minimize average energy consumption subject to completion probability

15. ESTIMedia 2005 15 ONLINE Adjustments When low load is detected, lower the task voltage –Preserving probabilistic performance Very small run-time expense –Few comparisons and arithmetic operations Load lower than expected Slow down further

16. ESTIMedia 2005 16 Stochastic Modeling Energy Management Scheme –OFFLINE Optimization –ONLINE Adjustments Experimental Results Conclusions OUTLINE

17. ESTIMedia 2005 17 Experimental Setup Compared with approaches for multiprocessor systems: –I (Zhang et. al, DAC2002 ) Ignores variability, correlations 100% completion Worst-case task load –II ( Hua et. al, EMSOFT2003 ) Ignores correlations Completion Probability Marginal load distribution Training set: 8 movie segments out of 10 Test set has 2 movies not included in training set. Three completion probabilities P CON –0.90, 0.95, 0.99 Two deadlines –Normal, Tight

18. ESTIMedia 2005 18 Experiment I : Normal Deadline 1. Significant energy savings 2. Desired completion probability achieved Avg E 860154100988331471009776412910091 Avg Pr 0.90260.95110.9899 Movie #P CON =0.90P CON =0.95P CON =0.99 IIIOFLNONLNIIIOFLNONLNIIIOFLNONLN

19. ESTIMedia 2005 19 Experiment II : Tight Deadline Avg E 100951009110070 Avg Pr 0.90300.95150.9898 II (Hua2003) fails with tight deadline – –Ignores correlations ONLN improves more Accurate stochastic model

20. ESTIMedia 2005 20 Experiment III: Comparison with GOD Single MovieOFFLINEONLINEGOD P CON = 0.991006652 P CON = 0.951008672 P CON = 0.901009276 GOD – –Ideal, Unrealizable, Non-causal – –For every individual frame Knows load of each task Computes optimal voltages There is still room for future work – –“application state” structure

21. ESTIMedia 2005 21Conclusions Demonstrated significant variability and correlations among workloads of MPEG2 tasks Our stochastic models capture essential characteristics of applications –Accurately predict performance Novel energy management scheme based on stochastic models –Significant energy savings

22. Soner Yaldiz, Alper Demir, Serdar Tasiran Koç University, Istanbul, Turkey Paolo Ienne, Yusuf Leblebici Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland Characterizing and Exploiting Task-Load Variability and Correlation for Energy Management in Multi-Core Systems ESTIMedia 2005 - Questions ?