Energy Efficient Prefetching and Caching Athanasios E. Papathanasiou and Michael L. Scott....
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Energy Efficient Prefetching and CachingAthanasios E. Papathanasiou and Michael L. Scott. University of Rochester Proceedings of 2004 USENIX Annual Technical Conference
Presenter: Ningfang MiJuly 01, 2005
Outline
Motivation New Energy-Aware Prefetching Algorithm
Basic idea Key challenges
Implementation in Linux kernel Evaluation results Conclusion
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
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
GD
Background(1)-- Fetch-on-Demand
A B C
A
A B C
B C
E F
D E F
D E F
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 idle idle idle idle idle
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?
HGD
Background (2) -- Traditional Prefetching (Cao’95)
A B C
A
A B C
B C
E F
D E F
D E F
Stream: A B C D E F G …. Access: 10 times units Fetch: 1 time unit
0 61
G H
I
G
61 time units
5 idle time intervals with 9 times units each
1 idle time intervals of 8
Idle Idle Idle Idle Idleidle
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
GD
Background (3)-- Energy-conscious
Prefetching
A B C
A
A B C
B C
E F
D E F
D E F
Idle4-30
Idle33-60
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
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
Key Challenges
When to prefech?
What to prefech?
How much to prefetch?
Key Challenges (1)-- When to Prefetch?In an epoch:
1. predict future accesses2. do prefetching
3. predict idle period4. if possible, go to sleep5. wake up for demand miss or prefetching or lo
w on memory
Estimate memory size for prefetching Free the required amount of memory Prefetch new data
idle
active
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
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
Implementation
In the Linux kernel 2.4.20 Hinted files Prefetch thread Prefetch cache Eviction Cache Handling write activity Power management policy
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
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 daemonsI/O activity
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 occurspage’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
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”
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 pr
edicted 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
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
Results (1)-- Idle Time Intervals
MPGE
concurrent SPHINX
make
80% >200
s
Standard kernel, 100% idle time less than 1 second, independent of memory sizeBursty system, larger memory sizes lead to longer idle interval lengths
Results (2)-- Energy Savings
Linux kernel Base case (64MB) Independent on me
mory size Bursty system
Depend on memory size
Significant energy saving when mem size is large
78.5%
77.4%
62.5%66.6%
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
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