a self-tuning configurable cache
DESCRIPTION
A Self-Tuning Configurable Cache. Ann Gordon-Ross and Frank Vahid* Department of Computer Science and Engineering University of California, Riverside *Also with the Center for Embedded Computer Systems at UC Irvine. - PowerPoint PPT PresentationTRANSCRIPT
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A Self-Tuning Configurable Cache
Ann Gordon-Ross and Frank Vahid*Department of Computer Science and Engineering
University of California, Riverside
*Also with the Center for Embedded Computer Systems at UC Irvine
This work was supported by the U.S. National Science Foundation, and by the Semiconductor Research Corporation
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Cache Power Consumption
Memory access: 50% of embedded processor’s system power
Caches are power hungry ARM920T (Segars 01) M*CORE (Lee/Moyer/Arends 99)
Thus, caches are a good candidate for optimizations
Different applications have vastly different cache requirements
Total size, line size, associativity
4KB 16 byte, 2-way
2KB 32 byte
direct-mapped 8KB 64 byte, 4-way
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Cache Tuning
Cache tuning is the process of determining the appropriate cache parameters for an application - can be done during runtime
Requires a tunable cache Cache parameter values can be varied during runtime
Requires tuning hardware Orchestrates cache tuning Energy
Executing in base configuration
Tunable cache
Tuning hw
TC Cache TuningTCTCTCTCTCTC TCTCTCTC
Download application
Microprocessor
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Online Cache Tuning
Reconfigure the cache dynamically to adapt to different phases of program execution or different applications in a multi-application environment
In this talk, I describe research that addresses when to reconfigure the cache for a periodic system
Feedback-control system for online cache tuning
Base cache energy
TimeEnergy Consumption
Phase-tuned
Change cache
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Online Cache Tuning Challenges
Base cache energy
TimeEnergy Consumption
Phase Interval
Base cache energy
TimeEnergy Consumption
Runtime energy
Tuning interval
Excess tuning energy
Tuning interval too short
Tuning interval too long
Base cache energy
TimeEnergy Consumption
Runtime energy
Tuning interval
Wasted energy in suboptimal configuration
Need a good tuning interval Tuning interval is the time between invocations of the
tuning hardware Should closely match phase interval - length of time
the system executes between phase changes
Problem: How does the tuning hardware determine when to invoke cache tuning - must
have knowledge of the future to obtain optimal results
Previous methods use a fixed tuning
interval and do not analyze the value chosen
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Periodic System - Fixed Phase Interval
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1m 2m 3m 4m 5m 6m 7m 8m 9m10m11
m12
m13
m14
m15
m16
m17
m18
m19
m20
m
Tuning interval (millions of cycles)
No
rma
lize
d e
ne
rgy
Online tuningenergynormalized tobase
Phase interval
fixed at 10 million cycles
Tuning interval too
short
Tuning interval too
long
Energy savings = 32% (includes 7% overhead due to tuning)
Base Line
Negative savings if tuning
interval is greater than
phase interval!
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Online Algorithms
Need to determine tuning interval while system is executing
Online algorithms process data piecemeal - unable to view entire dataset
Online tuner must be able to determine the tuning interval based on current and past events with no knowledge of future
Goal: Adjust tuning interval (TI) to match phase interval
Observe change in energy due to tuningCache tuning
No change in energyNo change in energy
TimeEnergy Consumption
TI TITI TI TI TI TI
Change in energy
Phase change occurred
Phase change occurred
TITI TITI TITI
No change in energy
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Online Cache Tuner – Feedback Control System
We model our online cache tuner as a feedback control system Controller Logic is based on attack/decay online algorithm
Draw on fuzzy logic to stabilize tuning interval
Change tuning interval based on how close or far the system is to being “stable”
Use a 2 part equation
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Controller Logic
%∆E
0100%M
ultiplicative change to
tuning interval (∆TI)
Stable System
PoS
1.0
Large energy change, tunes too
infrequently, decrease tuning interval (tuning
interval too long)
Small energy change, tunes too frequently, increase tuning interval (tuning interval too short)
U
D
If %∆E < PoS,
y 1 U
PoSx U
If %∆E >= PoS,
y D 11 PoS
x 1D 11 PoS
PoS %∆E averaged over last W
measurements to eliminate erratic behavior
Determine U, D, PoS and W through experimentation
%∆E
∆TI
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0
0.2
0.4
0.6
0.8
1
1.2Optimal Tuner Variable tuning interval
Online Cache Tuner Energy Savings
Base line
Observed similar results for less periodic systems and systems with more applications -
see paper for details.
29% energy savings - within 8% of optimalN
ormalized Energy
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Conclusions
Observed that fixed tuning intervals may not reveal energy savings Important to vary tuning interval to match system needs
Developed a feedback control system for online cache tuning 29% energy savings on average - 8% from optimal
Continuing work for more random systems