Recipe for ( In)Efficiency : Principles of Power-Aware Computing

1. Recipe for (In)Efficiency: Principles of Power-Aware Computing Or in other words, “Avoid Waste!” By ParthasarathyRanganathan Presented by: Jeffrey Boyd CSE 591: Green Computing 7 February 2011

2. “…longer battery life is often the highest-ranked metric in user studies of requirements for future mobile devices….” “The battery is often the largest and heaviest component of the system”

3. “…the total worldwide spending on power management for enterprises was likely a staggering $40 billion in 2009.” “The electricity consumption of computing equipment in a typical U.S. household runs to several hundred dollars per year”

4. Heat Management ∧ Power Management

5. “…increased densities will start hitting the physical limits of practical air-cooled solutions” “…there is a widening gap between advances in battery capacity and anticipated increases in mobile-device functionality.”

6. “…in the future, significant improvements in power and energy efficiency are likely to result from also rethinking algorithms and applications at higher levels of the solution stack.”

7. Waste #1: General-purpose solutions It’s cheaper to manufacture general-purpose devices Design is a “union” of maximum requirements Optimizing for both mission-critical and non-mission-critical systems Supporting legacy devices

8. Waste #2: Planning for Peaks and Growth • Lack of proportional computing • Optimizing for worst-case scenarios • Overprovisioning • Benchmarks stress worst-case performance workloads

9. Waste #3: Design Process Structure • Local optimization != global efficiency • Can happen when each layer makes worst-case assumptions about other layers • Organizational inefficiencies • What happens when one Power management is done by one department and the cooling infrastructure is handled by another? Miscommunications/Inefficiencies

10. Waste #4: Tethered-system Hangover The drive for even greater performance under the assumption of unlimited power Incremental performance improvements inconsistent with the additional power consumed

11. Savings #1: Power-efficient Alternative • Use more power-efficient components • SSD • Optics

12. Savings #2: Energy Proportionality • Turn off unused resources • Understand latency • Ensemble level • Changing traffic routing • Virtual machine consolidation

13. Savings #3: Match work to power-efficient option Match the task to the right-sized resource Implies a choice of resources

14. Savings #4: Overlap Energy Events • Combine multiple tasks into one energy-consuming event • Multiple reads on a single spin of a hard disk • Prefetching data • Shared cache • Decompose task into smaller subtask to avoid duplication

15. Savings #5: Focus on Required Functionality Don’t be general-purpose Incremental or modular improvements can be more energy efficient than monolithic future-proof designs

16. Savings #6: Cross Layers, Broaden Solution Space • Think global • Layers should communicate with each other • Temperature-aware workload placement

17. Savings #7: Trade off some other metric for energy Can your design tolerate some decrease in performance in order to have greater gains in power efficiency? DVD image fidelity vs. battery life Improved Efficiency vs. delay

18. Savings #8: Use common-case efficiency • Optimize for the most-likely state • Server power-supplies could be inefficient under heavy load, efficient under light loads

19. Savings #9: Spend Someone else’s Power Send computation from mobile device to server Scavenge energy

20. Savings #10: Spend power to Save Power Garbage collector Compression

21. 3 Common Elements • Rich measurement and monitoring infrastructure • Accurate analysis tools and models • Identify trends and relationships • Control algorithms and policies that coordinate power and heat control