A Framework for Dynamic Energy Efficiency and Temperature Management (DEETM)
-
Upload
leila-maldonado -
Category
Documents
-
view
11 -
download
0
description
Transcript of A Framework for Dynamic Energy Efficiency and Temperature Management (DEETM)
A Framework for Dynamic Energy Efficiency and
Temperature Management(DEETM)
Michael Huang, Jose Renau, Seung-Moon Yoo, Josep TorrellasUniversity of Illinois at Urbana-Champaign
http://iacoma.cs.uiuc.edu/flexram
Micro’33, Dec 2000 2
Motivation
Increasingly high power consumption– High temperature– Inefficient use of energy
Limitations of existing approaches– Static optimizations – Coarse grain dynamic management (DPM)– Inefficient temperature control (sleep)– Independent targets: temp, energy
efficiency
Micro’33, Dec 2000 3
Goal
Temperature: enforce limit while minimizing slowdown
Energy efficiency: maximize energy saving while exploiting performance slack
Unified framework for Dynamic Energy Efficiency and Temperature Management (DEETM)
Micro’33, Dec 2000 4
Contribution: DTEEM Framework
Existing limitations:
– static application
– coarse grain dynamic
– inefficient techniques
– independent targets: temperature
control energy efficiency
DEETM approach:
– multiple techniques
– dynamic
– fine grain
– order techniques for maximum efficiency
– unified target
Micro’33, Dec 2000 5
DEETM Framework
Monitors temperature & execution slack
Runs decision algorithm periodically: {Thermal, Slack} components
Activates low-power techniques – dynamically– incrementally– in prioritized order
Micro’33, Dec 2000 6
Techniques
Instruction filter cache
Data cache subbanking
Voltage scaling
Voltage scaling: DRAM
only
Light sleep
Micro’33, Dec 2000 7
Instruction Filter Cache
Filter Cache
Instruction Memory
Processor
Filter Cache
L1 Cache
ProcessorHigh power mode:
Time: 1
Energy: E Filter Cache
Instruction Memory
ProcessorHigh power mode:
Time: 1
Energy: E Filter Cache
Instruction Memory
Processor
Low power mode:
Time: 1 + mr*1
Energy: e + mr*E
Micro’33, Dec 2000 8
Techniques
Instruction filter cache
Data cache subbanking
Voltage scaling
Voltage scaling: DRAM
only
Light sleep
Micro’33, Dec 2000 9
Chip Environment
Processor-in-memory
64 lean processors– 2-issue static
Optimized memory hierarchy
Integrated thermal sensors and instruction counter
Processor
D-CacheI-Cache
DRAM Bank
Instruction Memory
ProcessorDRAM Bank
Interconnect
Micro’33, Dec 2000 10
Individual Techniques-E*D
IFilterSubBankVoltFreq
0.7
Nor
mal
ized
Ene
rgy-
Del
ay P
rodu
ct1.6
1.5
1.4
1.3
1.2
1.1
1.0
0.9
0.8
Normalized Average Power1.00.90.80.70.60.5
MemVoltSleep
Micro’33, Dec 2000 11
Combinations - E*D1.0
0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
0.9
0.8
0.7
0.6
0.5
Normalized Average Power
Nor
mal
ized
Ene
rgy-
Del
ay P
rodu
ct
VoltFreq
IFilterGradual
Micro’33, Dec 2000 12
Summary
Techniques ordered by efficiency (same order apply to both targets)
System applies techniques in order, dynamically and incrementally
Micro’33, Dec 2000 13
Thermal Algorithm
Tem p > MaxTem p
Tem p < M inTem p
Yes
No
Add the nextm ost effic ient
technique
Rem ove the lasttechniqueYes
Macrocycle
Micro’33, Dec 2000 14
Temperature Control - Limit
*1
* %25%75 iii PowerPowerPower
0
10
20
30
40
50
60
70
80
90
0.6 0.8 1 1.2Normalized Power* Limit
Mac
rocy
cle
Ove
r lim
it (%
)
Uncontrolled
Gradual
Micro’33, Dec 2000 15
Temperature Control - Slowdown
0
20
40
60
80
100
0.6 0.8 1 1.2
Normalized Power* Limit
Slow
dow
n (%
)
Sleep
Gradual
Micro’33, Dec 2000 16
Slack Algorithm
MacrocycleTest baseline IPC
Add technique; Testnew effective IPC*
Slowdownenough?
No
Adjust duty cycle oftechnique if necessary
YesEffective IPC*: frequency-adjusted
Microcycle
Every Macrocycle (HW/OS)
The First Few Microcycles (HW)
Micro’33, Dec 2000 17
Slack - Energy Consumption
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 5 10 15 20 25 30 35 40 45 50Slack (%)
Nor
mal
ized
Ene
rgy
E*D*D = Constant
Gradual
Micro’33, Dec 2000 18
Slack Misprediction
0.85
0.9
0.95
1
1.05
1.1
1.15
0 5 10 15 20 25 30 35 40 45 50Slack (%)
Use
d Sl
ack/
Slac
k
GradualPerfect
Micro’33, Dec 2000 19
Related Issues
Algorithm interaction
Selecting macrocycle
Handling Thermal Crisis situation
Reducing technique activation overhead
Flexible technique ordering
Hardware vs. software implementation
Micro’33, Dec 2000 20
Conclusions
Effective & efficient temperature control– very few macrocycles still over limit – 27% longer execution vs. 98% (by
sleeping)
Efficient & accurate fine-grain exploitation of execution slack– 5% slack 27% energy saved– small slack misprediction