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Reporter: Fu-Jiun Lu 2010-05-14

A Realistic Variable Voltage Scheduling Model for Real-Time Applications ICCAD Proceedings of the 2002 IEEE/ACM international conference. Reporter: Fu-Jiun Lu 2010-05-14. Abstract.

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Reporter: Fu-Jiun Lu 2010-05-14

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  1. A Realistic Variable Voltage Scheduling Model for Real-Time ApplicationsICCAD Proceedings of the 2002 IEEE/ACM international conference Reporter: Fu-Jiun Lu 2010-05-14

  2. Abstract Voltage scheduling is indispensable for exploiting the benefit of variable voltage processors. Though extensive research has been done in this area, current processor limitations such as transition overhead and voltage level discretization are often considered insignificant and are typically ignored. We show that for hard, real-time applications, disregarding such details can lead to sub-optimal or even invalid results.

  3. Outline • Introduction • Preliminaries • Basic Algorithm • Improved Algorithm • Experimental Results • Randomly generated job sets • CNC & Avionics • Summary

  4. Introduction The demands for mobile and pervasive computing devices have made low power computing a critical technology. One of the most effective ways of reducing energy is so called Dynamic Voltage Scaling(DVS). To effectively exploit the benefit provided by a variable voltage processor, careful selection of voltage levels and frequencies, often referred to as voltage scheduling, is crucial.

  5. Introduction (Cont.) • While substantial research to utilize this emerging technology, often there is a sizeable gap between the simulated environment and an actual, tangible implementation. • One such detail is voltage transition overhead • the time and energy overhead incurred whenever a voltage transition takes place. • Another detail is the discrete voltage levels • some variable voltage processors provide only a limited number of voltage levels.

  6. Preliminaries • A set of jobs :J={J1,…,Jn} • Release times :ri • Deadline :di • Worst case execution cycles:ci • transition interval :△t • variable transition energy overhead:△E (a) An example set of jobs. (b) Optimal voltage schedule with LPEDF

  7. Preliminaries (Cont.) • But S3’s speed has surpassed the normalized maximum of 1, so the required speed is unachievable. • Second, J3 will miss its deadline even if the speed of 1.5 is possible. From Fig.1(b) modified by inserting transition overhead.

  8. Basic Algorithm Fig.3: The voltage schedule obtained by simple modification of LPEDF. • To integrate the voltage transition timing overhead into LPEDF • to extend the critical interval to accommodate the timing overhead. • adjust its speed. • We propose the modification to LPEDF as follows: Instead of compressing just the critical interval, down to a single time point, we compress the interval and adjust the job sets accordingly.

  9. Basic Algorithm(Cont.) Fig.4: Job arrangements about the interval Ti squeezed down into a single time point ti. We will refer to thisproblem as monotonicity violation. Monotonicity violation occurswhen less time is available to execute the instructions injobs that overlap a transition interval.

  10. Improved Algorithm We improve the energy efficiency of Algorithm 1 and incorporate transition energy overhead and discrete voltage levels into considerations. Unnecessary energy may be wasted when using Algorithm 1. To prevent any deadlines from being missed, we next introduce the concept of the latest start time for a job set and an important lemma on how to compute it. tLS : the latest time at set of jobs J can begin execution at speed s* and still meet all deadlines in J .

  11. Improved Algorithm (Cont.) Lemma 1 helps us to find the latest start time for these jobs. Then, with a simple simulation of the execution, we can find the finish time for these jobs. Therefore, after a critical interval is identified in Algorithm 1, its speed is increased to the immediately higher available voltage level. Again, we can use Lemma 1 to find the minimal necessary interval with the given voltage.

  12. Experimental Results • Use the randomly generated job sets and real-world examples. • Applied the Algorithm 1 and the improved algorithm • overhead ranging from 0% to 100% of the average deadline of the jobs. • for a processor with 5 discrete levels as with the AMD processor, and a processor with 14 discrete levels as with the SA-1100 system. • Current experiments do not include energy overhead.

  13. Randomly generated job sets For the processor with 5 available voltage levels, improved algorithm can save nearly 50% of the energy compared with Algorithm 1 when the transition timing overhead is around 50% of the job deadline.

  14. CNC & Avionics The maximal energy saving can be as high as 17% for CNC example and 82.5% for Avionics example. The energy consumption grows rapidly with the longer transition overhead and fewer available voltage levels. Energy consumption for CNC. Energy consumption for Avionics.

  15. Summary We have shown through examples and analysis that limitations such as transition overhead or discrete voltage levels can cause a theoretically optimal schedule to become invalid if not correctly accounted for during the scheduling process. Currently the optimality of our algorithms is not guaranteed, so further algorithm development may improve results even more.

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