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Power Aware Scheduling for AND/OR Graphs in Multi-Processor Real-Time Systems Dakai Zhu, Nevine AbouGhazaleh, Daniel Mossé and Rami Melhem PARTS Group.

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Presentation on theme: "Power Aware Scheduling for AND/OR Graphs in Multi-Processor Real-Time Systems Dakai Zhu, Nevine AbouGhazaleh, Daniel Mossé and Rami Melhem PARTS Group."— Presentation transcript:

1 Power Aware Scheduling for AND/OR Graphs in Multi-Processor Real-Time Systems Dakai Zhu, Nevine AbouGhazaleh, Daniel Mossé and Rami Melhem PARTS Group Computer Science Department University of Pittsburgh Presenter: Dakai Zhu

2 ICPP'02http://www.cs.pitt.edu/PARTS2 Motivation Complex satellite and surveillance systems Real-time processing Limited energy Multi-Processor (homogenous/heterogeneous) Application: automated target recognition (ATR) Workload varies on different paths Traditional AND-model not enough AND/OR model Very few works on power management for multiprocessors Static: Gruian-2000 Dynamic: Yang-2001, Zhu-2001

3 ICPP'02http://www.cs.pitt.edu/PARTS3 Power Management Why & What: Power Management? Battery operated: Laptop, PDA and Cell phone Heating : complex servers (multiprocessors) Power Aware: maintain QoS, reduce energy How? Power off un-used parts: LCD, disk for Laptop Gracefully reduce the performance (CPU only) Dynamic power P d = C ef *V dd 2 *f [Chandrakasan92, Burd96] C ef : switch capacitance V dd : supply voltage f : processor frequency linear related to V dd

4 ICPP'02http://www.cs.pitt.edu/PARTS4 Power Aware Scheduling T1T1 D f max time Static Power Management (SPM) Static slack: uniformly slow all tasks [Weiser-1994, Yao-1995, Gruian-2000] T2T2 E Static Slack idle time T1T1 T2T2 T2T2 T1T1 0.6E Energy f T1T1 T2T2 time T1T1 T2T2 f max /2 E/4 Uniprocessors

5 ICPP'02http://www.cs.pitt.edu/PARTS5 Power Aware Scheduling (cont) T1T1 D f max time Dynamic Power Management (DPM) in uniprocessors Dynamic slack: non-worst execution 10% [Ernst-1994] DPM: [Krishna-2000, Kumar- 2000, Pillai-2001, Shin-2001] T1T1 T2T2 T2T2 f max /2 Static Slack idle E E/4 time T1T1 T2T2 f max /3 0.12E time T1T1 f max /2 Dynamic Slack Power Aware Scheduling Multi-Processors SPM: length of schedule over deadline DPM ???

6 ICPP'02http://www.cs.pitt.edu/PARTS6 Outline Motivation and Background AND/OR Model Application Greedy Algorithms and Slack Stealing Speculative Algorithms Evaluation and Analysis Conclusion

7 ICPP'02http://www.cs.pitt.edu/PARTS7 Application: AND/OR Model Real-Time Application Set of tasks Single Deadline Directed Acyclic Graph (DAG) Comp. (c i, a i ) AND (0,0) OR (0,0): probabilities T1T1 T2T2 T3T3 T4T4 T5T5 (1,2/3) (2,1)(1,1)(4,2)(3,2) T7T7 T6T6 (1,1) 60% 40% The sample application TiTi

8 ICPP'02http://www.cs.pitt.edu/PARTS8 Problem Statement System Model Multi-Processor (DVS) Shared Memory SM(Q, etc) DVS-CPU Scheduling Algorithm? DVS reduce energy Timing requirement Partition or Global? P P P P

9 ICPP'02http://www.cs.pitt.edu/PARTS9 Slack Stealing Shifting Static Schedule: 2-proc, D = time f T1T1 T4T4 T5T5 T7T7 D L0L0 T1T1 T4T4 T5T5 T7T7 f Shifting D L0L0 T3T3 T2T2 T6T6 L1L1 Recursive if embedded OR nodes T3T3 T2T2 T6T6 T1T1 T7T7 ` L1L1

10 ICPP'02http://www.cs.pitt.edu/PARTS10 Proposed Algorithms Greedy algorithm, two phases: Off-line: longest task first heuristic; Slack stealing via shifting Compute LST i for T i On-line: Same execution order Claim the slack: LST i – t i (t i LST i ) Compute speed:

11 ICPP'02http://www.cs.pitt.edu/PARTS11 Proposed Algorithms (cont) Actual Running Trace: left branch, T i use a i Possible Shortcomings Too greedy: slow fast Number of speed change (overhead) T7T7 f time D T6T6 Proposed Algorithms T1T1 L0L0 T3T3 T2T2 L1L1

12 ICPP'02http://www.cs.pitt.edu/PARTS12 Proposed Algorithms (cont) Optimal for uniprocessor: Single speed Energy – Speed: Concave Minimal Energy when all tasks SAME speed Speculation: statistical information about Application Static Speculation All tasks f i = max ( f ss, f g i ) Adaptive Speculation Remaining tasks f i = max ( f as, f g i ) Proposed Algorithms

13 ICPP'02http://www.cs.pitt.edu/PARTS13 Simulations Schemes Considered NPM: no power management (BASELINE), idle = 5%*P max GSS: greedy slack stealing SS: static speculation with greedy AS: adaptive speculation with greedy Parameters Number of processors Load: worst case time over deadline (global static slack L 0 ) Alpha: task average run time over WCET (dynamic slack) Overhead: 5us/change Processor Model Transmeta: 16 levels 200Mhz (1.10V)– 700Mhz (1.65V) Intel Xscale: 5 levels 150Mhz (0.75V)– 1 Ghz (1.80V)

14 ICPP'02http://www.cs.pitt.edu/PARTS14 Evaluation ATR on 2-processors, alpha =0.95, Overhead = 5 us/change Transmeta More Static Slack Less Static Slack

15 ICPP'02http://www.cs.pitt.edu/PARTS15 Evaluation (cont.) ATR on 2-processors, alpha =0.95, Overhead = 5 us/change Intel Xscale More Static Slack Less Static Slack

16 ICPP'02http://www.cs.pitt.edu/PARTS16 Evaluation (cont.) Transmeta Synthetic App. on 2-processors, load =0.8, Overhead = 5 us/change More Dynamic Slack Less Dynamic Slack

17 ICPP'02http://www.cs.pitt.edu/PARTS17 Evaluation (cont.) Intel Xscale Synthetic App. on 2-processors, load =0.8, Overhead = 5 us/change More Dynamic SlackLess Dynamic Slack

18 ICPP'02http://www.cs.pitt.edu/PARTS18 Conclusion and Contributions Conclusions: Significant energy saving with dynamic scheme. Greedy is good f min prevents the Greedy to be dumb Few speed levels reduces the probability of speed changes Contributions: Greedy Slack Stealing Algorithm Speculation Algorithms AND/OR

19 ICPP'02http://www.cs.pitt.edu/PARTS19 Questions


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