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Princeton University Electrical Engineering
12th International Symposium on High-Performance Computer Architecture
HPCA-12, Austin, TX. 2006
Feb 14, 2006
Phase Characterization for Power:
Evaluating Control-Flow-Based and Event-Counter-Based Techniques
Canturk ISCI
Margaret MARTONOSI
Canturk Isci - Margaret Martonosi2
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
What are Program Phases? Distinct and often-recurring regions of program behavior
Ex: Vortex
0.20.40.60.8
11.2
105 149 193 237 281 325 369 413 457 501 545
Billions of Instructions
IPC
0.4
0.6
0.8
1
105 149 193 237 281 325 369 413 457 501 545
Billions of Instructions
L2
Re
fs
00.20.40.60.8
1
0 44 88 132 176 220 264 308 352 396 440
Billions of Instructions
Me
m R
efs
Canturk Isci - Margaret Martonosi3
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
38
42
46
50
0 44 88 132 176 220 264 308 352 396 440Billions of Instructions
Po
we
r [W
]
Power Behavior has Phases, too
Recurring intervals of distinct power behavior
Ex: Vortex
Canturk Isci - Margaret Martonosi4
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Phase Methods for Power
Two main approaches:
Key Question:
How do these methods perform in terms of useful representations of power phase behavior?
Several studied program characteristics
Control Flow Methods Basic Block Vectors (BBVs)
[Sherwood et al. ASPLOS’02]
Event Monitoring Techniques Performance Monitoring Counters (PMCs)
[Isci and Martonosi Micro’03]
Canturk Isci - Margaret Martonosi5
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Outline
Real-system experimentation framework
How do the control-flow-based and event-counter-based approaches perform in power characterization?
Reasons why the two approaches can differ
Canturk Isci - Margaret Martonosi6
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
PinPintoolApplication
Binary
Application
Experimental Setup
OS
Hardware
Instrumentbasic block
heads
Sample basic block
head addresses
Collect PMC event rates
Enable/Disable counters
Read/Flush power history
Enable/Disablepower input
Performance CounterHardware
External Power
Measurement via Current
Probe
OS serial device
file
Canturk Isci - Margaret Martonosi7
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
A {BBV,PMC,Power} Sample
0x80485200x80485540x8048570
0x80487740x804878d0x804879c
Visited basic blocks:10844
PMCs:
463832
4862349
299303
36382
Power history:
35.9W36.9W
37.2W37.5W36.5W
BBV32
5 0 15 12 44 6
Hash
1 Sample
5
0
15
13
44
6
1 BBV
0.5
0.02
0.7
1.4
0.16
1 PMCvector
37W
1 Powernumber
13
0x8048554
Canturk Isci - Margaret Martonosi8
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
From Sample Vectors to Phases
We have vectors as proxy for power Like vectors => like power
Cluster similar vectors together and consider them a power phase
Here: First Pivot Clustering Paper also shows agglomerative clustering
Canturk Isci - Margaret Martonosi9
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Evaluation Main Steps
Cluster BBV samples
Cluster PMC vectors
Compare each to true measured power
Also compare to Oracle: classify directly for power
Random: assign samples to target clusters randomly
Benchmarks: 46 benchmark-input pairs from SPEC2K and other document
creation, media and scientific applications
Canturk Isci - Margaret Martonosi10
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Results
0%
5%
10%
15%
20%
25%
30%
AVE(SPECint) AVE(SPECfp) AVE(OTHER) AVE(Overall)
Pe
rce
nt
Err
or
w.r
.t. A
ctu
al P
ow
er
Random
BBV
PMC
Oracle
Consistent results regardless of clustering method SPECfp < SPECint < Others following from variability of power and memory
behavior
Canturk Isci - Margaret Martonosi11
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Errors with respect to Bounds
Consistent results regardless of clustering method SPECfp < SPECint < Others following from variability of power and memory
behavior BBV and PMCs both improve on upper bounds, but also significant gap over
lower bound
0%
5%
10%
15%
20%
25%
30%
AVE(SPECint) AVE(SPECfp) AVE(OTHER) AVE(Overall)
Pe
rce
nt
Err
or
w.r
.t. A
ctu
al P
ow
er
Random
BBV
PMC
Oracle
BBVs 70% of Random
PMCs 40% of Random
Canturk Isci - Margaret Martonosi12
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Errors with respect to Bounds
Consistent results regardless of clustering method SPECfp < SPECint < Others following from variability of power and memory
behavior BBV and PMCs both improve on upper bounds, but also significant gap over
lower bound
0%
5%
10%
15%
20%
25%
30%
AVE(SPECint) AVE(SPECfp) AVE(OTHER) AVE(Overall)
Pe
rce
nt
Err
or
w.r
.t. A
ctu
al P
ow
er
Random
BBV
PMC
Oracle
Oracle 30% of BBVs
Oracle 50% of PMCs
Canturk Isci - Margaret Martonosi13
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Comparing PMCs to BBVs
Consistent results regardless of clustering method SPECfp < SPECint < Others following from variability of power and memory
behavior BBV and PMCs both improve on upper bounds, but also significant gap over
lower bound PMCs generally lead to less errors than BBVs
0%
5%
10%
15%
20%
25%
30%
AVE(SPECint) AVE(SPECfp) AVE(OTHER) AVE(Overall)
Pe
rce
nt
Err
or
w.r
.t. A
ctu
al P
ow
er
Random
BBV
PMC
Oracle PMCs achieve 40%
less errors than BBVs
Canturk Isci - Margaret Martonosi14
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Different Target Number of Clusters
PMCs perform relatively better for the practical range of target clusters
Relative BBV error is significantly larger than PMCs for small number of phases [1-10]
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 20 40 60 80 100
AVE Error (BBV)
AVE Error (PMC)
Canturk Isci - Margaret Martonosi15
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Why BBV and PMC Phases Differ
Same Basic Blocks can have different power behavior
Different cache hit/miss patterns at different points
Operand dependent behavior
Different basic blocks can have similar power behavior
Different execution paths with effectively same execution characteristics
Canturk Isci - Margaret Martonosi16
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
1 51 101 151 201 251 301
10
20
30
40
50
60
Po
we
r [W
] H L M
1 51 101 151 201 251 301
10
20
30
40
50
60 H L M
0
0.2
0.4
0.6
0.8
1
4 12 30 51 73 94 115
1 51 101 151 201 251 301
10
20
30
40
50
60 H L M
0
0.2
0.4
0.6
0.8
1
4 12 30 51 73 94 115
Instructions [xBillion]
L2FPU
Effectively Same Execution Mesh: Various computationally similar tasks
Lead to many control-flow phases, not binding to application behavior
M1 M2 M3 M1 M2 M3 M1 M2 M3
BB
V P
atte
rns
PM
C E
ven
ts
Canturk Isci - Margaret Martonosi17
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Resulting Phases
For power management, too detailed control flow information can be detrimental!
1 51 101 151 201 251 30110
20
30
40
50
60
Po
we
r [W
] H L M
0
1
2
3
4
5
6
4 12 30 51 73 94 115
BBV Phases (N=5) PMC Phases (N=5)
M1 M2 M3 M1 M2 M3 M1 M2 M3
H L MDesiredPhases:
B AA C B AC B AC BBBV:
B A CPMC:
ObservedPhases:
BB
V P
att
ern
s
Canturk Isci - Margaret Martonosi18
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Potentials & Challenges Control flow (BBVs):
Perfect repeatability Architectural independence Detail at program level
Runtime applicability BBV phases ≢ power phases No physical binding to power
Event counters (PMCs):
Runtime monitoring Strong relation to power
Imperfect repeatability Lack of detail
Combining the strengths of two sides? Mutual information, but direct combination of vectors does not help! Future direction: Consider in terms of hierarchy/cooperation
Canturk Isci - Margaret Martonosi19
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Conclusions Phase characterizations with control flow and event counter
features can provide insight to application power behavior on real systems 2-6X better characterization than upper bounds
Two features can suggest significantly different power phase characterizations under different scenarios
PMC based approaches generally provide a better proxy to changes in power behavior 40% less errors than BBVs
Resulting experimental framework and observations can help guide phase-oriented characterization and system adaptation work on real systems
Canturk Isci - Margaret Martonosi22
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Thanks!
Canturk Isci - Margaret Martonosi23
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
EXTRAS 1.1) Why care about phases examples 1.2) Why care about pwr phases examples 1.3) What are different features that prev studies looked at? 2) Experiment setup details 3) Illustration of BBV, PMC & Power collection methodology 3.1) Similarity/Vector Distance concept 3.2) Error computation 3.3) Tracked Events 3.6) Results for agglomerative 3.7) More on Sources of contradiction 3.8) Explaining BBV patterns 3.9) Dcache example to varying data locality 3.95) Stream Example to Operand Dependent Behavior 3.96) 5 Phases to effectively same execution? 3.97) BBV+PMC Hierarchy 4) Power Behavior Under Pin 5) How bad are the sampling effects on BBVs? 10) How much power phases different from IPC phases?
(What is the scatter for IPC & PWR from measurement)
Canturk Isci - Margaret Martonosi24
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Characterizing execution regions
1.1) Why Care About Phases?
00.10.20.30.40.50.60.70.80.9
1
5 10 15 20 25Time [s]
E1 E2 E3 E4
Canturk Isci - Margaret Martonosi25
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
0
0.2
0.4
0.6
0.8
1
2 7 12Time [s]
Store Refs
Load Refs
Load Misses
Store Misses
Committed Instrns
1.1) Why Care About Phases? Characterizing execution regions
Managing dynamic adaptation
OFFON
Canturk Isci - Margaret Martonosi26
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
3 8 13Time [s]
Load Refs
Store Misses
1.1) Why Care About Phases? Characterizing execution regions
Managing dynamic adaptation
Use current phase/behavior to predict future behavior
Canturk Isci - Margaret Martonosi27
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
1.2) Why Care About Power Phases?
Useful for: Guiding power budget / temperature limit management
40
45
50
10 54 98
35
45
55
65
75
10 54 98
Slow down!
Power [W] Temp. [oC]
Time [s] Time [s]
Uncontrolled T
Enforced T
I.e. Montecito/Foxton
Canturk Isci - Margaret Martonosi28
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
1.2) Why Care About Power Phases?
Useful for: Guiding power budget / temperature limit management Power/Temperature aware scheduling
Time [s]
Po
wer
[W
]
[Bellosa et al. COLP’03]
Canturk Isci - Margaret Martonosi29
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
1.2) Why Care About Power Phases?
Useful for: Guiding power budget / temperature limit management Power/Temperature aware scheduling Power balancing for multiprocessor systems/activity migration
Power PowerTask1 Task2
Swap hot task
Slow down!Speed up!
Core/μP 1 Core/μP 2
Canturk Isci - Margaret Martonosi30
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
1.3) Evaluating Phase Methods for Power
All lead to different interesting characterizations
Several methods, looking at different similarity features
- Specific metrics (IPC, EPI)
- Hardware performance vectors
- Branch counts
- Working sets
- Basic block vectors
- Procedures
From event monitors
From (sampled) control flow
We specifically look at basic block vectors (BBVs) & performance counter based (PMC) vectors
How do these behave in terms of power representation?
Is there a dominant method or does a combination work better?
Canturk Isci - Margaret Martonosi31
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
2) Experimental Framework Goal: To acquire control flow, performance metric and
power behavior of workload execution at matching & controlled observation points on a real system
Control flow: Sampled PC Basic block vectors (BBVs)
Performance related events: Performance monitoring counters (PMCs) PMC vectors
Power: External measurements via current probe/DMM Verification
Canturk Isci - Margaret Martonosi32
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Pin
Analy
sis
Inst
rum
enta
tio
n
2) Setup
Application Binary
Performance CounterHardware
External Power
Measurement via Current
Probe
OS serial device
file
Experimental
Machine
Instrumentbasic block
heads
Sample basic block
head addresses
Collect PMC event rates
Start/stop/reset
counters
Read/Flush power history
Detach/Attachpower input
Canturk Isci - Margaret Martonosi33
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Pin
Experimental Setup
Application Binary
Performance CounterHardware
External Power
Measurement via Current
Probe
OS serial device
file
Experimental
Machine
Instrumentbasic block
heads
Sample basic block
head addresses
Collect PMC event rates
Start/stop/reset
counters
Read/Flush power history
Detach/Attachpower input
Canturk Isci - Margaret Martonosi34
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
2) Experiment Setup Details PIN kit 1795 3 level Trace instrumentation
~Every user trace: Conditional inlined trace count Every 50-200K Trace call: Sample EIP Every 5-20M Trace call:
Generate BBV & Collect PMCs & Read PWR history
Constraint: Instrumentation should not overwhelm Power variations!!
BBV Generation: Sample BBL heads hash into 32 dimensions
PMC Reading: Single rotation subset of 15 Sample via syscalls at major instrumentation & reset for next
Power Reading: Read from serial device buffer Disable device at major instrumentation & exhaust buffer
Canturk Isci - Margaret Martonosi35
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
3) Methodology IllustrationExecution timeline:
0x80485200x80485540x8048570
0x80487740x804878d0x804879c
Visited basic blocks:
Powerhistory:
00003
PMCs:
00005
00004
00008
00007
00003
1st level analysis:
2nd level analysis:
3rd level analysis:
Instr-n count = 5
BBV = 0 0 0 0 0 0
Canturk Isci - Margaret Martonosi36
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
3) Methodology IllustrationExecution timeline:
0x80485200x80485540x8048570
0x80487740x804878d0x804879c
36.5W
Visited basic blocks:
Powerhistory:
00009
PMCs:
00015
00013
00023
00040
00007
1st level analysis:
2nd level analysis:
3rd level analysis:
Instr-n count = 11
BBV = 0 0 0 0 0 0
Canturk Isci - Margaret Martonosi37
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
3) Methodology IllustrationExecution timeline:
0x80485200x80485540x8048570
0x80487740x804878d0x804879c
36.5W
Visited basic blocks:
Powerhistory:
00024
PMCs:
00033
00019
00043
00080
00015
1st level analysis:
2nd level analysis:
3rd level analysis:
Instr-n count = 18
BBV = 0 0 0 0 0 0
Canturk Isci - Margaret Martonosi38
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
3) Methodology IllustrationExecution timeline:
0x80485200x80485540x8048570
0x80487740x804878d0x804879c
37.2W
Visited basic blocks:
Powerhistory:
01024
PMCs:
34033
04519
993455
05373
00345
1st level analysis:
2nd level analysis:
3rd level analysis:
Instr-n count ~ 1M
BBV = 0 0 0 0 0 0
37.5W36.5W
2nd level analysis: H32
1 2
Canturk Isci - Margaret Martonosi39
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
3) Methodology IllustrationExecution timeline:
0x80485200x80485540x8048570
0x80487740x804878d0x804879c
37.2W
Visited basic blocks:
Powerhistory:
01024
PMCs:
34033
04519
993455
05373
00345
1st level analysis:
2nd level analysis:
3rd level analysis:
Instr-n count ~ 1M
BBV = 0 0 1 0 0 0
37.5W36.5W
Canturk Isci - Margaret Martonosi40
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
1 Sample
3) Methodology IllustrationExecution timeline:
0x80485200x80485540x8048570
0x80487740x804878d0x804879c
35.9W
Visited basic blocks:
Powerhistory:
10844
PMCs:
463832
58479
4862349
299303
36382
1st level analysis:
2nd level analysis:
3rd level analysis:
Instr-n count ~ 100M
BBV = 5 0 15 13 44 6
36.9W
37.2W37.5W36.5W
3rd level analysis:
5
0
15
13
44
6
1 BBV
0.5
0.02
0.7
1.4
0.16
1 PMCvector
37W
1 Powernumber
Canturk Isci - Margaret Martonosi41
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
3.1) What is the similarity of a vector? We use L1 (Manhattan) Distance
Simple Example: Consider 4 vectors,
each with 4 dimensions:
1
2
3
5
2
1
5
3
2
2
4
4
4
3
5
1
322214543
:2 &1 VectorsBetween Distance
:nCalculatio DistanceManhattan Exemplary
0 6 3 7
6 0 3 6
3 3 0 7
7 6 7 0
Log all distances in the similarity matrix:
For 2 Target Phases: {0,1,2} & {3}
Canturk Isci - Margaret Martonosi42
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
3.2) Error Computation For 5 Target Phases:
Representative Power:
Mean of power values for each phase
Error for a benchmark:
RMS error over all samples after phase classification, with respect to the representative power of that phase
Single number measure of how much power variation remains within each phase, averaged-nonuniformly-over all phases.
Canturk Isci - Margaret Martonosi43
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
3.3) Tracked EventsPMC Event Mask Description
IOQ_allocation 0x0EFE1 I/O Queue and Bus Sequence Queue allocations from all agents
BSQ_cache_ref 0x0507 L2 cache read and write accessesFSB_data_activity 0x03F Front Side Bus utilization for reading,
driving or reserving the bus.ITLB_reference 0x07 ITLB translations performeduop_queue_writes 0x07 All μops written to the μop queueTC_deliver_mode 0x038 Number of cycles the processor is
buiding traces from instruction decodeuop_queue_writes 0x04 μops written to the μop queue by
microcode ROMx87_FP_uop 0x08000 All x87 floating point μops executedLD_port_replay 0x02 Number of replays at the load portx87_SIMD_moves 0x018 Executed x87, MMX, SSE and SSE2
load, store and register move μops ST_port_replay 0x02 Number of replays at the store portbranch_retired 0x0F All branches retireduops_retired 0x03 Number of μops retired front_end_event 0x03 Number of loads and stores retireduop_type 0x06 Tags load and stores (Does not count)
Canturk Isci - Margaret Martonosi44
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
3.6) Results
Consistent results regardless of clustering method SPECfp < SPECint < Others following from variability of power and memory
behavior BBV and PMCs perform improve on upper bounds, but also significant gap over
lower bound PMCs generally lead to less errors than BBVs
0
2
4
6
8
10
12
AVE(SPECint) AVE(SPECfp) AVE(OTHER)
Err
or
[W]
Upper Bound
BBV
PMC
Lower Bound
0
2
4
6
8
10
12
AVE(SPECint) AVE(SPECfp) AVE(OTHER)
Err
or
[W]
Upper Bound
BBV
PMC
Lower Bound
First pivot Agglomerative(compl.)
Canturk Isci - Margaret Martonosi45
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
3.7) Sources of Contradiction between Control Flow and Performance Metrics Dynamic change in data locality
Sort a 10K / 10M entry (presorted/random)
More prominent with TP/DB applications
Effectively same execution Scientific/computational programs
Compute similarity between PMC / PC vector samples
Operand dependent behavior Overflow/Operand width dependent execution
More to be observed with power-aware architectural choicesi.e. Pentium M – Execution width scaling
[Wu et al. Micro’05]
[Gochman et al. ITJ Q2’03]
Canturk Isci - Margaret Martonosi46
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
3.7) The Reasons of Contradiction No global observation explains all cases
Memory subsystem Memory boundness
Dynamically varying data locality
Number of traversed basic blocks
Effectively same execution
Operand dependent behavior
CoD ~ 0.61
0%
2%
4%
6%
8%
10%
12%
14%
0 10000 20000 30000 40000BBL Count
BB
V E
rro
r
Canturk Isci - Margaret Martonosi47
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
1 51 101 151 201 251 301
10
20
30
40
50
60
Po
we
r [W
] H L M
0
0.2
0.4
0.6
0.8
1
4 12 30 51 73 94 115
Instructions [xBillion]
L2FPU
3.8) Effectively Same Execution Mesh: Various computationally similar tasks
Lead to many control-flow phases, not binding to application behavior
M1 M2 M3 M1 M2 M3 M1 M2 M3
30
5
65
32
6
62
33
4
63
0:4033:
630
...
0:6032:
620
0:5030:
650
Canturk Isci - Margaret Martonosi48
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
0.0%
20.0%
40.0%
60.0%
80.0%
100.0%
100% 90% 80% 70% 60% 50% 40%
Specified Hit Rate
L1
Hit
Rat
e
Expected
From Counters
3.9) Motivating Ex: Dcache Microkernel Specify L1 hit rate, generate desired hits via random linked list
traversal
A
C
M
P
Z
L1 S
ize
L2 S
ize
Mem
Siz
e
Canturk Isci - Margaret Martonosi49
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
3.9) Dcache: Control flow Sample PC every 1M instructions ± 100, map to basic blocks
134514480
134514490
134514500
134514510
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134514530
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2.5E+09 9.8E+09 1.7E+10 2.4E+10
0x8048736
0x8048753
0x8048766
0x8048773
0x804878D
L1 Intensive L2 Intensive Mem Intensive
Canturk Isci - Margaret Martonosi50
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
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3.9) Dcache: Performance counters and power
Sample PMCs every 100M instructions, collect power from current probe
L1 Intensive L2 Intensive Mem Intensive
Canturk Isci - Margaret Martonosi51
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
2.5 13.7 24.8 36.0 47.1 58.2 69.4 80.5 91.7
Instructions [xBillion]
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Overflow at iteration 261
3.95) Operand Dependent Behavior Stream: 4 repetitive operations Reaches OVF after 261 iterations
BBVsequences:
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Canturk Isci - Margaret Martonosi52
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
3.95) Operand Dependent Behavior
Drastic change in power behavior
Not seen in control flow, but is followed by PMC vectors
Timeline shows the actual impact
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Overflow at iteration 261
Canturk Isci - Margaret Martonosi53
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
3.96) Resulting Phases BBVs distinguish
all different regions of operation
However, the distance between the M phases still larger than the distance between H, L and M3 even for N=3
Too much granularity conceals the available information
1 51 101 151 201 251 30110
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BBV Phases (N=5) PMC Phases (N=5)
M1 M2 M3 M1 M2 M3 M1 M2 M3
B DC E D E D E D
B DA C B DC B DC E
B A C
B AA C B AC B AC B
H L MDesiredPhases:
5 Phases:
PMC:
BBV:
3 Phases:
PMC:
BBV:
Canturk Isci - Margaret Martonosi54
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
3.97
Canturk Isci - Margaret Martonosi55
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
3.97) BBV+PMC Hierarchy, an EX:
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6570
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BA C D B C D BA C D B C D
Canturk Isci - Margaret Martonosi56
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
3.97) BBV+PMC Hierarchy, an EX:
BA C D B C D BA C D B C D
PMCs: ?
BA A C
BA A B
Knowledge of BBV repetition information helps detect/predict actual recurrent behavior:
Canturk Isci - Margaret Martonosi57
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
3.97) Flagging ‘Problem’ Phases
Same control-flow with varying behavior can be identified to avoid false predictions based on this recurrence
30
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65
30
5
65
?
Canturk Isci - Margaret Martonosi58
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
4) Gcc Native/Pin Powers
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Time [s]
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wer
[W
]
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[W
]
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[W
]
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Po
wer
[W
]
(a) Native execution power behavior without instrumentation
(b) Flattened power behavior with Pin basic block instrumentation
Instrumentation dominant
Execution dominant
(c) Improved external power behavior with Pin trace instrumentation and conditional inlining
(d) Power behavior assigned to application execution by Pintool
Canturk Isci - Margaret Martonosi59
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
30
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0 44 88 132 176 220 264 308 352
Po
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]
4) Power Results with Pin Do we still have the hook on power variability?
Native From PIN
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0 44 88 132 176 220 264 308 352 396 440 484
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0 44 88 132 176 220 264 308 352 396
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0 44 88 132 176 220 264 308 352 396 440 484 528 572 616 660
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0 12 24 36 48 60 72
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]
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0 44 88 132 176 220
Mcf
Vortex
Gzip
Canturk Isci - Margaret Martonosi60
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
5) Sampling Effect on BBVs Is sampling good enough? Are they Meaningful?
Full Blown BBV SimMatrices
Our sampled & hashed BBV Simmatrices
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
5) Similarity Matrix Example
Consider 4 vectors, each with 4 dimensions:
1
2
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5
2
1
5
3
2
2
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4
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3
5
1
0 6 3 7
6 0 3 6
3 3 0 7
7 6 7 0
322214543
:2 &1 VectorsBetween Distance
:nCalculatio DistanceManhattan Exemplary
Log all distances in the similarity matrix
0 6 3 7
6 0 3 6
3 3 0 7
7 6 7 0
Color-scale from black to white (only for upper diagonal)
Canturk Isci - Margaret Martonosi62
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
5) Interpreting Similarity Matrix Plot
Level of darkness at any location (r,c) shows the amount of similarity between vectors –samples– r & c.
i.e. 0 & 2
All samples are perfectly similar to themselves
All (r,r) are black
Vertically above the diagonal shows similarity of the sample at the diagonal to previous samples
i.e. 1 vs. 0
Horizontally right of the diagonal shows similarity of the sample at the diagonal to future samples
i.e. 1 vs. 2,3
Canturk Isci - Margaret Martonosi63
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
A B C D
AB
CD
0
50
100
150
200
250
300
350
400#
of
Ph
as
e P
air
s a
t (X
,Y)
c
5) Full vs. sampled BBV Phases Comparison with Gzip
Sampled BBV Phases
Full-blown BBV Phases
Canturk Isci - Margaret Martonosi64
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
10) Why Need Power Phases? (Power ≡? IPC)
How different is characterization for IPC from power?
Canturk Isci - Margaret Martonosi65
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
10) Why Need Power Phases? (Power ≡? IPC)
Bzip2-graphic: very strong relation between power and IPC domains
0
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1
1.5
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2.5
20 30 40 50 60 70
Power [W]
IPC
bzip2
Canturk Isci - Margaret Martonosi66
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
10) Why Need Power Phases? (Power ≡? IPC)
Swim: Still strong relation , but the power and IPC domains across benchmarks are different (different relation)
0
0.5
1
1.5
2
2.5
20 30 40 50 60 70
Power [W]
IPC
swim
Canturk Isci - Margaret Martonosi67
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
10) Why Need Power Phases? (Power ≡? IPC)
Mcf: Similar power range as bzip2 achieved with much lower IPC domains
0
0.5
1
1.5
2
2.5
20 30 40 50 60 70
Power [W]
IPC
mcf
Canturk Isci - Margaret Martonosi68
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
10) Why Need Power Phases? (Power ≡? IPC)
Ghostscript: Similar IPC can lead to drastically different power ranges within a benchmark at the lower end
0
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1
1.5
2
2.5
20 30 40 50 60 70
Power [W]
IPC
gs
Canturk Isci - Margaret Martonosi69
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
10) Why Need Power Phases? (Power ≡? IPC)
Lame: Similar observation at higher end, with distinct IPC domains overlapping power
0
0.5
1
1.5
2
2.5
20 30 40 50 60 70
Power [W]
IPC
lame
Canturk Isci - Margaret Martonosi70
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
10) Why Need Power Phases? (Power ≡? IPC)
Art: The actual spread due to measurement is much smaller!
0
0.5
1
1.5
2
2.5
20 30 40 50 60 70
Power [W]
IPC
art
Canturk Isci - Margaret Martonosi71
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Ditched Slides
Canturk Isci - Margaret Martonosi72
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Phase Analysis & Real Systems Phases: Self-similar, mostly recurrent, execution regions
Useful for characterization, dynamic-adaptive management SimPoints [Sherwood et al., ASPLOS’02]
Multiconfigurable HW [Dhodapkar and Smith, ISCA’02]
Real systems impose additional constraints Larger granularities O(ms)
Applicability to large-scale management methods
Dynamic voltage/frequency scaling
Thermal Management
Identifying recurrence under inexact replication of repetitive behavior!
Canturk Isci - Margaret Martonosi73
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Phase Analysis & Real Systems Phases:
Self-similar, mostly recurrent, execution regions
Real-system experiments Long execution timescale observations Incorporating system effects / verification with real measurements
Useful for characterization, dynamic-adaptive management
0
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E1 E2 E3 E4 E5
Canturk Isci - Margaret Martonosi74
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Phases Distinct and often-recurring regions of program behavior
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0 5 10 15 20 25 30 35 40 45Time [s]
Store Refs
Load Refs
Load Misses
Store Misses
CommittedInstrns
Canturk Isci - Margaret Martonosi75
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
What are Program Phases? Distinct and often-recurring regions of program behavior
0
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5 10 15 20 25Time [s]
Mem Refs
L3 Refs
IPC
Canturk Isci - Margaret Martonosi76
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Why Care About Phases?
0
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E1 E2 E3 E4 E5
Characterizing execution regions
Canturk Isci - Margaret Martonosi77
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
0
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2 7 12Time [s]
Store Refs
Load Refs
Load Misses
Store Misses
Committed Instrns
Why Care About Phases? Characterizing execution regions
Managing dynamic adaptation
Canturk Isci - Margaret Martonosi78
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Why Care About Phases? Characterizing execution regions
Managing dynamic adaptation
Use current phase/behavior to predict future behavior
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Store Refs
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Store Misses
Canturk Isci - Margaret Martonosi79
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Power Phases
Recurring intervals of distinct power behavior
Ex: Vortex
40
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0 44 88 132 176 220 264 308 352 396 440
Power [W]
Billions of Instructions
Canturk Isci - Margaret Martonosi80
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Evaluating Phase Methods for Power
All lead to different interesting characterizations
We specifically look at basic block vectors (BBVs) & performance counter based (PMC) vectors
How do these behave in terms of power representation?
Is there a dominant method or does a combination work better?
Similarity Based On:
Metrics(IPC, EPI, etc)
Hardware Performance
Vectors
BBVs, Working
SetsProcedures Branches
Sampling Quanta:
Instruction/Code/Time/Energy intervals
From performance monitoring counters
From (sampled) control flow
Canturk Isci - Margaret Martonosi81
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Outline
Background and Definitions
Real-system experimentation framework
How do the control-flow-based and event-counter-based approaches perform in power characterization?
Reasons why the two approaches can differ
Canturk Isci - Margaret Martonosi82
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Experimental Framework Goal: To acquire control flow, performance metric and power
behavior of workload execution at matching & controlled observation points on a real system These will guide phase classification of control flow and event based
behavior with validation against power measurements
Control flow: From sampled PC
Will construct basic block vectors (BBVs) for each observed sample
Performance related events: From performance monitoring counters (PMCs)
Will construct PMC vectors for each sample
Power: From external measurements via current probe/DMM
Will provide the actual power behavior for each observed sample for verification
Canturk Isci - Margaret Martonosi83
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Experimental Setup
Application Binary
Application
OS
Hardware
Pin
Instrumentbasic block
heads
Sample basic block
head addresses
Collect PMC event rates
Start/stop/reset
counters
Read/Flush power history
Detach/Attachpower input
Performance CounterHardware
External Power
Measurement via Current
Probe
OS serial device
file
Canturk Isci - Margaret Martonosi84
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Pin
Experimental Setup
Application Binary
Application
OS
Hardware
Instrumentbasic block
heads
Sample basic block
head addresses
Collect PMC event rates
Enable/Disable counters
Read/Flush power history
Enable/Disablepower input
Performance CounterHardware
External Power
Measurement via Current
Probe
OS serial device
file
Canturk Isci - Margaret Martonosi85
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Phase Classification For each benchmark:
N sets of {BBV32,PMC15,Power}
Cluster BBV32 and PMC15 into F sets using L1-distance of vectors
Control-flow & event-counter based phases
Clustering methods: First Pivot: Simple, online method modified for a fixed target
number
Agglomerative: Complex, offline method. Link pairs of clusters based on linkage criterion
Average linkage
Complete linkage
Canturk Isci - Margaret Martonosi86
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Phase Classification For each benchmark: N sets of {BBV32,PMC15,Power}
Cluster BBV32 and PMC15 using L1-distance of vectors
Control-flow & event-counter based phases
Clustering methods: First Pivot, Agglomerative [Average & Complete linkage]
First Pivot:
Canturk Isci - Margaret Martonosi87
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
From Sample Vectors to Phases
If we knew power precisely We could divide power into ranges and classify points directly
But we have vectors as proxy for power Like vectors => like power
Cluster similar vectors together and consider them a power phase
Here: First Pivot Clustering Paper also shows agglomerative clustering
Canturk Isci - Margaret Martonosi88
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Evaluation Compute classification error for each method with sample
standard deviation of power
Upper and lower bounds: Oracle: classify directly for power
Random: assign samples to target clusters randomly
Benchmarks: 46 benchmark-input pairs from SPEC2K and other document
creation, media and scientific applications
Canturk Isci - Margaret Martonosi89
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Results
Consistent results regardless of clustering method
SPECfp < SPECint < Others following from variability of power and memory behavior
0
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8
10
12
AVE(SPECint) AVE(SPECfp) AVE(OTHER) AVE(Overall)
Err
or
[W]
Random
BBV
PMC
Oracle
Canturk Isci - Margaret Martonosi90
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Evaluation and Results
Consistent results regardless of clustering method SPECfp < SPECint < Others following from variability of power and memory
behavior
0%
5%
10%
15%
20%
25%
30%
AVE(SPECint) AVE(SPECfp) AVE(OTHER) AVE(Overall)
Pe
rce
nt
Err
or
w.r
.t. A
ctu
al P
ow
er
Random
BBV
PMC
Oracle
BBVs 12.9%
PMCs 7.3%
BBVs 1.9%
PMCs 1.5%
BBVs 5.6%
PMCs 3.3%
Canturk Isci - Margaret Martonosi91
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Errors with respect to Bounds
Consistent results regardless of clustering method SPECfp < SPECint < Others following from variability of power and memory
behavior BBV and PMCs both improve on upper bounds, but also significant gap over
lower bound
0%
5%
10%
15%
20%
25%
30%
AVE(SPECint) AVE(SPECfp) AVE(OTHER) AVE(Overall)
Pe
rce
nt
Err
or
w.r
.t. A
ctu
al P
ow
er
Random
BBV
PMC
Oracle
BBVs 3.3X of Oracle
PMCs 1.9X of Oracle
Canturk Isci - Margaret Martonosi92
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Resulting Phases BBVs distinguish all
different regions of operation
However, the distance between the M phases still larger than the distance between H, L and M3 even for N=3
Too much granularity conceals the available information
1 51 101 151 201 251 30110
20
30
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50
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] H L M
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4 12 30 51 73 94 115
Ph
as
e N
o
BBV Phases (N=5) PMC Phases (N=5)
-1
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3
4
4 12 30 51 73 94 115
Instructions [xBillion]
Ph
as
e N
o
BBV Phases (N=3) PMC Phases (N=3)
M1 M2 M3 M1 M2 M3 M1 M2 M3
Canturk Isci - Margaret Martonosi93
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Effectively Same Execution Mesh: Various computationally similar tasks
Lead to many control-flow phases, not binding to application behavior
1 51 101 151 201 251 301
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Instructions [xBillion]
L2FPU
M1 M2 M3 M1 M2 M3 M1 M2 M3
Canturk Isci - Margaret Martonosi94
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Control flow (BBVs): Repeatability
Architecturally independent
Runtime applicability [sampling, mapping to BBLs]
Managing dimensions
False alarms with effectively same execution behavior
Misses on varying data locality and operand dependent behavior
Event counters (PMCs): Runtime applicability
Imperfect repeatability
Managing variable event ranges
Lack of detail
Combining the strengths of two sides? They have mutual info, but direct combination of vectors does not help!
Future direction: Consider in terms of hierarchy
Potentials & Challenges with the Phase Characterization Approaches
Canturk Isci - Margaret Martonosi95
Phase Characterization for Power: Evaluating Control-Flow-Based and Event-Counter-Based Techniques
[HPCA-12 ’06]
Background: Power and Phases
Runtime processor power monitoring and estimation [Micro’03] Sample PMCs to estimate powers for 22 chip components
Real measurement feedback for tuning and verification
Workload power phase behavior with power vectors [WWC’03] Consider power estimations as power vectors
Characterize “power phases” based on vector similarity
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