1. Energy Efficient Prefetching and Caching Athanasios E. Papathanasiou and Michael L. Scott. University of Rochester Proceedings of 2004 USENIX Annual Technical Conference Presenter: Ningfang Mi July 01, 2005

2. Outline Motivation New Energy-Aware Prefetching Algorithm  Basic idea  Key challenges Implementation in Linux kernel Evaluation results Conclusion

3. Motivation Prefetching and caching in modern OS  A smooth access pattern improves performance Increase throughput Decrease latency What about energy efficiency?  A smooth access pattern results in relatively short intervals of idle times Idle times too short to save energy Spin-up time is not free

4. New Design Goal Maximize energy efficiency  Create a bursty access pattern  Maximize idle interval length  Maximize utilization when disk is active  Not degrade performance Focus on hard disks

5. G D Background(1) -- Fetch-on-Demand ABC A ABC BC EF DEF DEF Stream: A B C D E F G …. Access: 10 times units Fetch: 1 time unit 0 66 66 time units 6 idle time intervals with 10 times units each idle

6. Background (2) -- Traditional Prefetching (Cao ’ 95) Aim -- minimize execution time Four rules 1. Optimal Prefetching Prefetch the next referenced block not in cache 2. Optimal Replacement Discard the block whose next reference is farthest 3. Do no harm Never replace A with B when A be referenced before B 4. First Opportunity Never do prefect-and-replace later What to prefetch or discard? When to prefetch?

7. H G D Background (2) -- Traditional Prefetching (Cao ’ 95) ABC A ABC BC EF DEF DEF Stream: A B C D E F G …. Access: 10 times units Fetch: 1 time unit 0 61 GH I G 61 time units 5 idle time intervals with 9 times units each 1 idle time intervals of 8 Idle idle

8. Background (3) -- Energy-conscious Prefetching Replace “First opportunity” with  Maximize Disk Utilization Always initiate a prefetch when there are blocks available for replacement  Respect Idle Time Never interrupt a period of inactivity with a prefetch operation unless unless prefetching is urgent

9. G D Background (3) -- Energy-conscious Prefetching ABC A ABC BC EF DEF DEF Idle 4-30 Idle 33-60 61 time units 1 idle time intervals of 27 1 idle time intervals of 28 0 61 Stream: A B C D E F G …. Access: 10 times units Fetch: 1 time unit

10. Energy-Aware Prefetching -- Basic Idea Design guideline  Fetch as many blocks as possible when the disk is active  Not prefetch until the latest opportunity when the disk is idle. Epoch-Based Extensions to Linux Memory Management System  Divide the time into epochs  Each epoch: an active phase and an idle phase

11. Key Challenges When to prefech? What to prefech? How much to prefetch?

12. Key Challenges (1) -- When to Prefetch? In an epoch: 1. predict future accesses 2. do prefetching 3. predict idle period 4. if possible, go to sleep 5. wake up for demand miss or prefetching or low on memory Estimate memory size for prefetching Free the required amount of memory Prefetch new data idle active

13. Key Challenges (2) -- What to Prefetch? Prediction is based on hints.  Hint interface: File Specifier X Pattern Specifier +Time Information New applications submit hints to OS using new system calls  Monitor Daemon Provide hints automatically on behalf of applications Track file activity Access Analysi s Hint Generatio n

14. Key Challenges (3) -- How much to Prefetch? Decide # of pages be freed in active phase  The reserved memory be large enough to contain all predicted data accesses.  Prefetching not cause the eviction of pages that are going to be accessed sooner than the prefetched data First miss during idle phase  Compulsory Miss: A miss on a page without prior information  Prefetch Miss: A miss on a page with a prediction (hint)  Eviction Miss: A miss on a page be evicted for prefetching

15. Implementation In the Linux kernel 2.4.20  Hinted files  Prefetch thread  Prefetch cache  Eviction Cache  Handling write activity  Power management policy

16. Hinted Files Disclosed by:  Monitor daemon or applications  Kernel for long sequential file accesses Maintained in a doubly linked list Sorted by estimated first access time

17. Prefetch Thread Coordinating across applications  A lack of coordination limits idle interval length Issuing read/write from concurrently running applications during the same small window of times  Write: the update daemon  Page-out: the swap daemon  Prefetch/read: the prefetch daemon Generate prefetch requests for all running applications Coordinating three daemons I/O activity

18. Prefetch Cache & Eviction Cache Extend LRU with Prefetch Cache  Contain pages requested by the prefetch daemon  Timestamp: when the page will be accessed  When a page is referenced or its timestamp is exceeded, move it to the standard LRU list Eviction Cache: Stores eviction history  Metadata of recently evicted pages  Eviction number: # of pages that have been evicted  When an eviction miss occurs page’s eviction number - epoch’s starting eviction number => # of pages that were evicted without causing an eviction miss => Estimate prefetch cache size for next epoch

19. Handle Write Activity In the original kernel, update daemon runs every 5 sec and flushes dirty buffers > 30 sec  => the idle interval <= 5 seconds Now, a modified update daemon flushes dirty buffers once per minute.  A flag in the extended open system call indicates dirty buffers can be delayed until the corresponding file is closed the process opening the file exits  The monitor daemon provides guideline to OS “flush-on-close” or “flush-on-exit”

20. Power Management Policy Power management policy based on the prediction of the next idle length  Set the disk to Standby within 1 sec after idle if predicted length > Standby breakeven time The problem of mispredictions  Actual idle time < Standby breakeven time  Return to a dynamic-threshold spin-down policy Ignore predictions until the accuracy increases Avoid harmful spin-down operations

21. Evaluation Used Hitachi hard disk  three low power modes Workloads:  MPEG playback (MPEG)  MP3 encoding and MPEG playback (Concurrent)  kernel compilation (Make)  speech recognition system (SPHINX) Metrics  Length of idle periods: make longer  Energy savings  Slowdown: minimize performance penalties

22. Results (1) -- Idle Time Intervals MPGE concurrentSPHINX make 80% >200 s Standard kernel, 100% idle time less than 1 second, independent of memory size Bursty system, larger memory sizes lead to longer idle interval lengths

23. Results (2) -- Energy Savings Linux kernel  Base case (64MB)  Independent on memory size Bursty system  Depend on memory size  Significant energy saving when mem size is large 78.5% 77.4% 62.5% 66.6%

24. Results (3) -- Execution Time Successfully avoid delay caused by disk spin-up ops An increased cache hit ratio improves the performance <2.8% <1.6% 4.8% 15% Increased paging and disk congestion <5% Increased cache hit ratio speeds the time

25. Conclusion Energy-conscious prefetching algorithm  Maximize idle interval length  Maximize energy efficiency  Minimize performance penalties Experimental results  Increase the length of idle intervals  Save 60-80% disk energy USENIX'04 Best Paper Award  http://www.cs.rochester.edu/u/papathan/research/ BurstyFS