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Characterizing and Exploiting
Task-Load Variability and Correlation
for Energy Management
in Multi-Core Systems
Soner Yaldiz , Alper Demir, Serdar Tasiran
Koç University, Istanbul, Turkey
Paolo Ienne, Yusuf Leblebici
Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
ESTIMedia 2005
Multi-Core Soft Real-Time Systems
• Chip-level multiprocessing for massive performance
– Energy management problem
• Real-time multimedia applications
– Audio, video processing
• Soft real-time systems
– Tolerance to deadline misses
processors
T1
task graph
T2
start
end
T3
MPEG2 video frames
t
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T4
time
t + TDEADLINE
2
Variability and Correlation
• This work: First approach to consider variability and
correlations for multiprocessor energy management
variability
Task1
workload
workload
Task2
• Capture by Stochastic Models
• Exploit for Energy Management
Voltage
Taski
positive
correlation
workload
probability
V1
V2
– Dynamic Voltage Scaling (DVS)
deadline
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Time
3
Motivating Example
• Application composed of two tasks on a single processor
T1
start
T2
end
TDEADLINE = 2 sec
• Task loads low (2) or high (10) with equal probability
probability
T1,T2
50%
2
50%
10
instructions
• Processor Operating Modes
– Slow Mode -> 6 instructions-per-second
– Fast Mode -> 10 instructions-per-second
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Task Load Combinations
probability
T1
start
T2
T1,T2
end
TDEADLINE = 2 sec
50%
2
50%
10
instructions
Probabilities for task load combinations:
Independent
T1
T2
2
10
25% 25%
10 25% 25%
2
Positively Correlated
T1
T2
2
10
2
50% 0
10
0 50%
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Negatively Correlated
T1
T2
2
10
0 50%
10 50%
0
2
5
Motivating Example
Independent
Application
–
2 tasks
Processor modes
–
–
Slow 6 inst/sec
Fast 10 inst/sec
Deadline
–
2 sec
Target
Positively
Correlated
T1
T2
2
2
10
25% 25%
10 25% 25%
1.0
0.75
Positive Correlation
Target
100%
Assumption Independent
Reality
2
10
T1
T2
2
10
2
50%
0
2
0
50%
10
0
50%
10 50%
0
never happens !
Slow mode -> 12 instructions in 2 sec
Misses desired performance
Assumption Independent
Reality
T1
T2
0.50
75%
Negatively
Correlated
Negative Correlation
Fast mode -> 20 instructions in 2 sec
Suboptimal energy
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OUTLINE
• Stochastic Modeling
• Energy Management Scheme
– OFFLINE Optimization
– ONLINE Adjustments
• Experimental Results
• Conclusions
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Stochastic Modeling Flow
start
T1
T2
OBSERVATIONS
Task1
Task2
1
2
10
2
5
5
3
2
5
4
10
2
5
5
5
6
2
10
7
2
5
8
5
5
end
• Computational Demand (CD) of a task
– Number of CPU cycles for execution
• Demands are represented by dist
– Quantized for manageability
• dist is obtained from a set of traces
• Demand of tasks constitutes an
‘observation’
– (T1,T2) = ( 5, 5 ) observed 3 out of 8.
– dist ( 5,5 ) = 3/8
T1
T2
dist
5
10
2
2/8
2/8
5
3/8
10
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1/8
8
Case Study: MPEG2
•
MPEG2 video decoding
•
Slice-based task decomposition(Olukotun et.al,1998)
– Widely-used and computationally intensive
– VLD ( Variable-length decoding)
– MC ( Motion compensation )
VLD0, MC0
VLD1, MC1
VLD2, MC2
...
..
.
Data Precedence
Experimental Data:
–
–
–
–
Task Assignment
10 movie segments
19 slices, 38 tasks
24 frames-per-second
~ 14000 frames per movie
Processor Precedence
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Variability of MPEG2 Task Loads
2- Longaggregate
Tails
aggregate
Worst-Case causes overdesign
1- Similarity
Traning set predicts workload
for others
one movie
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one movie
10
Correlation among MPEG2 Task Loads
High Correlation
aggregate
statistics
one movie
...
...
...
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...
11
Critical Path
•
•
•
Summation of worst-case task loads
: 64 million cycles
Observed worst-case total load
: 28 million cycles
Ignoring correlations lead to far from optimal
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OUTLINE
• Stochastic Modeling
• Energy Management Scheme
– OFFLINE Optimization
– ONLINE Adjustments
• Experimental Results
• Conclusions
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OFFLINE: Optimization Formulation
• Each task i has fixed voltage Vi for all periods
• GOAL: Determine optimal Vi’s
minimize
average energy consumption
subject to
completion probability
• Nonlinear constrained optimization problem with 38 variables
– One voltage per task
• Stochastic programming formulation
– Based on stochastic application model
• Optimized voltages stored for run-time look-up
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ONLINE Adjustments
• When low load is detected, lower the task voltage
– Preserving probabilistic performance
• Very small run-time expense
– Few comparisons and arithmetic operations
Load lower than expected
Slow down further
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OUTLINE
• Stochastic Modeling
• Energy Management Scheme
– OFFLINE Optimization
– ONLINE Adjustments
• Experimental Results
• Conclusions
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Experimental Setup
•
Compared with approaches for multiprocessor systems:
– I (Zhang et. al, DAC2002 )
• Ignores variability, correlations
• 100% completion
• Worst-case task load
– II ( Hua et. al, EMSOFT2003 )
• Ignores correlations
• Completion Probability
• Marginal load distribution
•
Training set: 8 movie segments out of 10
•
Test set has 2 movies not included in training set.
•
Three completion probabilities PCON
– 0.90, 0.95, 0.99
•
Two deadlines
– Normal , Tight
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Experiment I : Normal Deadline
Movie #
PCON=0.90
I
Avg E
Avg Pr
II
860 154
PCON=0.95
PCON=0.99
OFLN
ONLN
I
II
OFLN
ONLN
I
II
100
98
833
147
100
97
764
129
0.9026
1. Significant energy savings
0.9511
OFLN ONLN
100
91
0.9899
2. Desired completion probability achieved
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Experiment II : Tight Deadline
• II (Hua2003) fails with
tight deadline
– Ignores correlations
• ONLN improves more
• Accurate stochastic model
Avg E
Avg Pr
100
95
0.9030
100
91
0.9515
100
70
0.9898
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Experiment III: Comparison with GOD
Single Movie
OFFLINE
ONLINE
GOD
PCON = 0.99
100
66
52
PCON = 0.95
100
86
72
PCON = 0.90
100
92
76
• GOD
– Ideal, Unrealizable, Non-causal
– For every individual frame
• Knows load of each task
• Computes optimal voltages
• There is still room for future work
– “application state” structure
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Conclusions
• Demonstrated significant variability and
correlations among workloads of MPEG2 tasks
• Our stochastic models capture essential
characteristics of applications
– Accurately predict performance
• Novel energy management scheme based on
stochastic models
– Significant energy savings
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- Questions ?
Characterizing and Exploiting
Task-Load Variability and Correlation
for Energy Management
in Multi-Core Systems
Soner Yaldiz , Alper Demir, Serdar Tasiran
Koç University, Istanbul, Turkey
Paolo Ienne, Yusuf Leblebici
Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
ESTIMedia 2005