Identifying Program Power Phase Behavior Using Power Vectors

Canturk Isci & Margaret Martonosi

WWC-6

10.27.2003Austin, TX

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Power Phase BehaviorPower Phase Behavior

Existence of distinguishable intervals during an application’s execution lifetime such that:

They share significantly higher resemblance within themselves in terms of power behavior the application exhibits on a given processorThis similarity is carried out by not only the total processor power, but also the distribution of power into processor sub-units

(Filtered) VPR Power Breakdowns

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(Filtered) Gcc Power Breakdowns

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Our Power Phase AnalysisOur Power Phase AnalysisGoal:

Identify phases in program power behavior

Determine execution points that correspond to these phases

Define small set of power signatures that represent overall power behavior

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Our ApproachOur ApproachOur Approach – Outline:

Collect samples of estimated power values for processor sub-units <Power Vectors> at application runtime

Define a power vector similarity metric

Group sampled program execution into phases

Determine execution points and representative signature vectors for each phase group

Analyze the accuracy of our approximation

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MotivationMotivationCharacterizing power behavior:

Future power-aware architectures and applications

Dynamic power/thermal management

Architecture research

Utilizing power vectors:

Direct relation to actual processor power consumption

Acquired at runtime

Identify program phases with no knowledge of application

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Vector(0) Vector(3) Vector(6) Vector(9) Vector(12) Vector(15) Vector(18) Vector(21) Vector(24)

RETIRE

Schedule Inst Queue2

Inst Queue1

Rename Allocation

Ucode ROM

1st Level BPU Trace Cache

Inst Dec

FP Regfile INT Regfile FP Exec

INT Exec

Data TLB MEM control

MOB

L1 cache ITLB & Fetch

2nd Level BPU L2 Cache

Bus Control

Generating Power VectorsGenerating Power Vectors

POWERCLIENT

POWERSERVER Voltage readings

via RS232 to logging machine

Convert voltage to measured power Convert access rates to component powers

1mV/Adc conversion

Counter based access rates

over ethernet

22 Entries of each power vector sample

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Power Vector Similarity MetricPower Vector Similarity Metric How to quantify the ‘power behavior

dissimilarity’ between two execution points?1. Consider solely total power difference

2. Consider manhattan distance between the corresponding 2 vectors

3. Consider manhattan distance between the corresponding 2 vectors normalized

4. Consider a combination of (2) & (3)

Construct a “similarity matrix” to represent similarity among all pairs of execution points

Each entry in the similarity matrix:

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Gcc Component Power Breakdowns

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Gcc Total Power

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MEASURED POWER COUNTER ESTIMATED POWER

Gzip Total Power

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MEASURED POWER COUNTER ESTIMATED POWER

Gzip Component Power Breakdowns

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Gcc Component Power Breakdowns

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Gcc Elaboration: Very variant power

Almost identical power behavior at 30, 50, 180s.

Although 88s, 110s, 140s, 210s and 230s show similar total power; 88, 210 and 230 share higher similarity.

Gcc Total Measured Power

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Gcc Total Power

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MEASURED POWER COUNTER ESTIMATED POWER

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Gzip Elaboration: Much regular power behavior

Spurious similarities are again distinguished by the similarity analysis

GZIP Total Measured Power

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Gzip Total Power

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MEASURED POWER COUNTER ESTIMATED POWER

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Grouping Execution PointsGrouping Execution Points “Thresholding Algorithm”:

Define a threshold of similarity < % of max dissimilarity>

Start from first execution point (0,0) and identify ones in the fwd execution path that lie within threshold for both normalized and absolute metrics

Tag the corresponding execution points (j,j) as the same group

Find next untagged execution point (r,r) and do the same along forward path

Rule: A tagged execution point cannot add new elements to its group!We demonstrate the outcome of thresholding with Grouping Matrices

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Gzip Grouping MatricesGzip Grouping Matrices

Gzip has 974 power vectors Cluster vectors based on similarity

using “thresholding” Max Gzip power dissimilarity: 47.35W

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Gzip Group Distribution for Threshold = 1%

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Generated Group DistributionsGenerated Group Distributions

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Representative Vectors & Execution PointsRepresentative Vectors & Execution Points

We have each execution point assigned to a group For Each Group:

For Each Execution Point:

We can represent whole execution with as many power vectors as the number of generated groups

Define a representative vector as the average of all instances of that group

Select the execution point that started the group (The earliest point in each group)

Assign the corresponding group’s representative vector as that point’s power vector

Assign the power vector of the selected execution point for that group as that point’s power vector

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Rec

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RECONSTRUCTED GZIP POWER for Threshold=1% <254 Vectors>

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TOTAL_MEASURED_POWER TOTAL_MODELED_POWER RECONSTRUCTED_POWER(Representative Vectors)

RECONSTRUCTED GZIP POWER for Threshold=10% <33 Vectors>

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TOTAL_MEASURED_POWER TOTAL_MODELED_POWER RECONSTRUCTED_POWER(Representative Vectors)

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RECONSTRUCTED GZIP POWER for Threshold=10% <33 Vectors>

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TOTAL_MEASURED_POWER TOTAL_MODELED_POWER RECONSTRUCTED_POWER(Vectors Based on Selected Execution Points)

RECONSTRUCTED GZIP POWER for Threshold=1% <254 Vectors>

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TOTAL_MEASURED_POWER TOTAL_MODELED_POWER RECONSTRUCTED_POWER(Vectors Based on Selected Execution Points)

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Component Power CharacterizationsComponent Power Characterizations

GZIP Modeled Power - Vector Components

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Bus Control L2 Cache 2nd Level BPU ITLB & Fetch L1 cache MOB MEM control Data TLB INT Exec FP Exec INT Regfile FP Regfile Inst Dec Trace Cache 1st Level BPU Ucode ROM Allocation Rename Inst Queue1 Inst Queue2 Schedule RETIRE TOTAL_MODELED_POWER

GZIP Reconstructed Power - Vector Components

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Bus Control_R L2 Cache_R 2nd Level BPU_R ITLB & Fetch_R L1 cache_R MOB_R MEM control_R Data TLB_R INT Exec_R FP Exec_R INT Regfile_R FP Regfile_R Inst Dec_R Trace Cache_R 1st Level BPU_R Ucode ROM_R Allocation_R Rename_R Inst Queue1_R Inst Queue2_R Schedule_R RETIRE_R RECONSTRUCTED_POWER

(Representative Vectors)GZIP Reconstructed Power - Vector Components

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Bus Control_R L2 Cache_R 2nd Level BPU_R ITLB & Fetch_R L1 cache_R MOB_R MEM control_R Data TLB_R INT Exec_R FP Exec_R INT Regfile_R FP Regfile_R Inst Dec_R Trace Cache_R 1st Level BPU_R Ucode ROM_R Allocation_R Rename_R Inst Queue1_R Inst Queue2_R Schedule_R RETIRE_R RECONSTRUCTED_POWER

(Vectors Based on Selected Execution Points)

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GZIP Reconstructed Power - Absolute Errors

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(Vectors Based on Selected Execution Points)GZIP Reconstructed Power - Absolute Errors

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(Representative Vectors)

Approximation ErrorApproximation Error

Due to thresholding algorithm Errors for selected exec. points are bounded with the threshold Max Error: 4.71W & RMS Error: 3.08W

As representative vectors are group centroids Cumulative errors for repr. vectors are lower Max Error: 7.10W & RMS Error: 2.31W

Error in total power< Σ(Component errors)

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ConclusionConclusion Presented a power oriented methodology to

identify program phases that uses power vectors generated during program runtime

Provided a similarity metric to quantify power behavior similarity of different execution samples

Demonstrated our representative sampling technique to characterize program power behavior

Can be useful for power & characterization research: Power Phase identification/prediction Reduced power simulation Dynamic power/thermal management

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Related WorkRelated Work Dhodapkar and Smith [ISCA’02]

Working set signatures to detect phase changes Sherwood et. al. [PACT’01,ASPLOS’02,ISCA’30]

Similarity analysis based on program basic block profiles to identify phases

Todi [WWC’01] Clustering based on counter information to identify

similar behavior

Our work in comparison Power oriented Power behavior similarity metric Runtime No information about the application is required Bounded approximation error with thresholding

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EOP

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Power Vector Components More detail on Power Vectors

Different Similarity Metrics Similarity matrices and equations for all discussed techniques

Similarity & Grouping Matrices Exemplified description of the two matrices and plots

Current & Future Research Discussion of the ongoing and future research Includes also some new ideas and some things to do to make

our current analysis solid

Questions & Rebuttals Starts with the discussion of presented work Discusses some shortcomings, things that need to be done to

improve and to verify that it is unique and solid(Some parts of current work also discusses these issues)

Also provides some answers to reviewers’ questions Includes some possible new ideas

EXTRA SLIDESEXTRA SLIDES

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Defining ComponentsDefining Components

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P4 Architecture vs LayoutP4 Architecture vs Layout

Components to Model:

1) Bus Control2) L2 Cache3) 2nd Level BPU4) ITLB & Ifetch5) L1 Cache

6) MOB7) Mem Control8) DTLB9) Int EXE10)FP EXE11) Int RF

12)FP RF13)Decode14)Trace $15)1st Level BPU16)Microcode ROM17)Allocation

18)Rename19) Inst-n Qs20)Schedule21) Inst-n Qs22)Retirement

Back Back

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Defining Events Defining Events Access Rates Access Rates We determined 24 events to approximate access rates

for 22 components Used Several Heuristics to represent each access rate

Examples:

Need to rotate counters 4 times to collect all event data Used 15 counters & 4 rotations to collect all event data

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Access Rates Access Rates Component Powers Component Powers

“Performance Counter based Access Rate estimations are used as proxy for max component power weighting together with microarchitectural details in order to estimate processor sub-unit powers”

EX: Trace cache delivers 3 uops/cycle in deliver mode and 1 uop/cycle in build mode:

Power(TC)=[Access-Rate(TC)/3 + Access-Rate(ID)] x MaxPower(TC) + Non-gated TC CLK power

Total power is computed as the sum of all 22 component powers + measured idle power (8W):

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Counter Access HeuristicsCounter Access Heuristics 1) BUS CONTROL:

No 3rd Level cache BSQ allocations ~ IOQ allocations Metric1: Bus accesses from all agents

Event: IOQ_allocationCounts various types of bus transactions

Should account for BSQ as wellaccess based rather than duration

MASK:Default req. type, all read (128B) and write (64B) types, include OWN,OTHER and PREFETCH

Metric2: Bus Utilization(The % of time Bus is utilized)Event: FSB_data_activity

Counts DataReaDY and DataBuSY events on BusMask:

Count when processor or other agents drive/read/reserve the busExpression: FSB_data_activity x BusRatio / Clocks Elapsed

To account for clock ratios

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Counter Access HeuristicsCounter Access Heuristics 2) L2 Cache:

Metric: 2nd Level cache referencesEvent: BSQ_cache_reference

Counts cache ref-s as seen by bus unitMASK:

All MESI read misses (LD & RFO)2nd level WR misses

3) 2nd Level BPU: Metric 1: Instructions fetched from L2 (predict)

Event: ITLB_ReferenceCounts ITLB translations

Mask:All hits, misses & UC hits

Metric 2: Branches retired (history update)Event: branch_retired

Counts branches retiredMask:

Count all Taken/NT/Predicted/MissP

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Counter Access HeuristicsCounter Access Heuristics 4) ITLB & I-Fetch:

etc……… 10) FP Execution:

Metric: FP instructions executedevent1: packed_SP_uop

counts packed single precision uopsevent2: packed_DP_uop

counts packed single precision uopsevent3: scalar_SP_uop

counts scalar double precision uopsevent4: scalar_DP_uop

counts scalar double precision uopsevent5: 64bit_MMX_uop

counts MMX uops with 64bit SIMD operandsevent6: 128bit_MMX_uop

counts integer SSE2 uops with 128bit SIMD operandsevent7: x87_FP_UOP

counts x87 FP uopsevent8: x87_SIMD_moves_uop

counts x87, FP, MMX, SSE, SSE2 ld/st/mov uops Back Back

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Similarity Based on Total PowerSimilarity Based on Total Power

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Similarity Based on Absolute Power Similarity Based on Absolute Power VectorsVectors

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Similarity Based on Normalized Power Similarity Based on Normalized Power VectorsVectors

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Similarity Based on Both Absolute and Similarity Based on Both Absolute and Normalized Power VectorsNormalized Power Vectors

Back Back

Similarity Matrix ExampleSimilarity Matrix Example

Consider 4 vectors, each with 4 dimensions:

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:2 &1 VectorsBetween Distance

:nCalculatio DistanceManhattan Exemplary

Log all distances in the similarity matrix

0 6 3 7

6 0 3 6

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7 6 7 0

Color-scale from black to white (only for upper diagonal)

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Interpreting Similarity Matrix PlotInterpreting Similarity Matrix Plot

Back Back

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

Grouping Matrix ExampleGrouping Matrix Example

Consider same 4 vectors:

0 6 3 76 0 3 63 3 0 77 6 7 0

togethergrouped becan 5.3 distance with VectorsThreshold 50%

together grouped becan none 0.7, Threshod 10%

7 :VectorsBetween Distance Maximum

Mark execution pairs with distance ≤ Threshold

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0 6 3 76 0 3 63 3 0 77 6 7 0 Back Back

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Current & Future ResearchCurrent & Future Research

FOLLOWING SLIDES DISCUSS ONGOING RESEARCH RELATED TO POWER PHASES. PLANS FOR FUTURE RESEARCH ARE ALSO DISCUSSED

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THE BIG PICTURETHE BIG PICTURE

Performance Monitoring

Real Power Measurement

PowerModeling

ThermalModeling

Performance Monitoring

Real Power Measurement

PowerModeling

ThermalModeling

PowerPhases

ProgramProfiling

ProgramStructure

To Estimate component power & temperature breakdowns for P4 at runtime…

To analyze how power phase behavior relates to program structure

Bottom line…

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PowerModeling

PowerPhases

ProgramProfiling

ProgramStructure

PowerPhases

ProgramProfiling

ProgramStructure

Phase BranchPhase Branch

Power Phase Behavior Similarity Based on Power Vectors Identifying similar program regions

Profiling Execution Flow Sampling process’ execution “PCsampler” LKM

Program Structure Execution vs. Code space Power Phases Exec. Phases

NOT YET?

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PowerModeling

PowerPhases

ProgramProfiling

ProgramStructure

PowerPhases

POWER PHASE BEHAVIORPOWER PHASE BEHAVIOR

Power Phase Behavior Similarity Based on Power Vectors Identifying similar program regions

Profiling Execution Flow Sampling process’ execution “PCsampler” LKM

Program Structure Execution vs. Code space Power Phases Exec. Phases

NOT YET

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Identifying Power PhasesIdentifying Power PhasesMost of the methodology is ready

Complete Gzip case in WWCExtensibility to other benchmarks

Generated similarity metrics for several Performing phase identification with

thresholdingRepeatibilty of the experimentSeveral other possible ideas such as:

Thresholding + k-means clustering Two-pass thresholding PCA for dimension reduction (or SVD?) Manhattan = L1 norm

Euclidian (L2) – not interestingChebyschev (Linf) - ??

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PowerModeling

PowerPhases

ProgramProfiling

ProgramStructure

ProgramProfiling

Program Execution ProfileProgram Execution Profile

ProgramStructure

Power Phase Behavior Similarity Based on Power Vectors Identifying similar program regions

Profiling Execution Flow Sampling process’ execution “PCsampler” LKM

Program Structure Execution vs. Code space Power Phases Exec. Phases

NOT YET

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Program Execution ProfileProgram Execution Profile

Sample program flow simultaneously with power Our LKM implementation: “PCsampler”

Not Finished…

Generate code space similarity in parallel with power space similarity

Relative comparisons of methods for: Complexity Accuracy Applicability, etc.

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CURRENT STATECURRENT STATE

Sample PC Binding to functionsReacquire PID

Those SPECs, Runspec: always in fixed address at ELF_program interpreterBenches: change pid between datasets

Verify PC with objdump So we can make sure it is the PC

we’re sampling

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Initial Data: PC?? Trace For gzip-sourceInitial Data: PC?? Trace For gzip-source

Gzip-Source PC Distribution

134515000

134520000

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0 20 40 60 80 100 120 140 160Sample

Me

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Correspond to:<send_bits><bit_reverse><lm_init><longest_match><fill_window>

functions

Back Back

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Generality of the technique All Spec benchmarks show

distinct phase behavior:Repeatability of the experiment

Need to be able to arrive at similar phase behavior in order to characterize an application

Correlation between vector components Inherent redundancy in power vectors Could be removed with PCA

Alternative norms for similarityApplicability of selected execution points

DiscussionDiscussionFrom here onFrom here on

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Similarity Analysis for Other Similarity Analysis for Other Applications?Applications? SPECs show similar applicable behavior

Not always phase-like, i.e. twolf has more like a power gradient

Results for other benchmarks: <NOT READY> Gcc & Twolf:

# of groups w.r.t. thresholdsErrors plots for reconstructed & selected vectors

Apply to other applications: Desktop applications

Will follow the bursty behavior, maybe determine action signatures??

Saving, computation, streaming, etc. Ghostscript might be interesting

Correlation between phases vs. locations of images

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Dependence of Results to the Dependence of Results to the Applied Power Model?Applied Power Model?The generic technique requires only

sufficiently detailed power breakdowns that add up to total power Doesn’t matter how you acquire the ‘power

vectors’ otherwiseIf you use some other characterization

data other than power vectors Can still perform phase analysis, but cannot

provide a direct estimation for reconstructed power behavior

Maybe use some kind of mapping?Log measured power data as well as characterization metrics?

Would still be unable to predict component-wise

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Possibility for Other Processors?Possibility for Other Processors?

Most recent processors are keen on power management There will be enough power variability to exploit for power

phase analysis Porting the power estimation to other architectures

Requires significant effort to Define power related metricsImplement counter reader and power estimation user and kernel SW

Porting to same architecture, different implementation More straightforward

Reevaluate max/idle/gated power estimates Experiences with other architectures:

Castle project for Pentium Pro (P6) Few watts of variationLow dimensionality

IBM Power3 IIVery low measured power variation

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Other Statistical Techniques?Other Statistical Techniques?

Alternative measures of distance: Different norms

Canberra Distance

Squared Chi-squared distance, etc.Other similarity metrics:

Pearson’s correlationcoefficient

Cosine similarity

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SPEClite vs. SimPoints SPEClite vs. SimPoints vs. Power Vectorsvs. Power VectorsApproaches of 3 methods:

SPEClite: Performance counts (runtime) (Perf. Oriented) (k-means partitioning for vectors normalized to 0 mean and unit variance) (PCA to create reduced vectors) (selects vectors closest to centroids)

SimPoints: Basic block accesses (Simulation) (Perf. Oriented) (k-means partitioning for normalized basic block vectors) (selects vectors closest to centroids –except for ‘Early’ Simpoints)

Power Vectors:Component power consumptions (Runtime) (Power Oriented) (threshold based partitioning for ‘normalized + absolute’ vectors) (Selects earliest vector of each group)

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Why Power Vectors w.r.t. Others?Why Power Vectors w.r.t. Others?

Provides a direct interpretation for power consumption Could be used to identify specific power behavior for

dynamic power/thermal management Power phases might be not a perfect

translation of performance phases <CURRENT WORK INVESTIGATES>

I.e. same basic block accesses during different architectural states

Generated at runtime Easy repeatability, etc.

Thresholding provides upper bound estimate for the power approximation with selected execution points

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Modified StuffModified Stuff

FOLLOWING SLIDES I MODIFIED FROM THE ORIGINAL, BUT STILL KEEP ‘EM

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Our Power Phase AnalysisOur Power Phase Analysis Goal:

Identify phases in program power behavior Determine execution points

that correspond to these phases Define small set of power signatures

that represent overall power behavior

Our Approach: Collect samples of estimated power values for processor sub-

units <Power Vectors> at application runtime Define a similarity metric regarding these power vectors Process the outcome of this similarity metric to group sampled

execution points (/Power Vectors) into phases Determine execution points and representative signature

vectors for each phase group Quantify the closeness of our approximation based on these

vectors to original power behavior

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MotivationMotivation Characterizing power behavior:

Future power-aware architectures and applications Dynamic power/thermal management

Multiconfigurable hardwareThread scheduling, DVS, DFSRecurring phase prediction

Architecture researchRepresentative –reduced– simulation points

Utilizing power vectors: Direct relation to actual processor power consumption Acquired at runtime

Similarity relations generated quicklyEasy repeatability for different datasets/compilationsIdentify (recurring) phases over large scales of execution

Identify program phases with no knowledge of application (i.e. no basic block profile, PC sampling, code space info, etc.)

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Power Vector Similarity MetricPower Vector Similarity Metric How to quantify the ‘power behavior

dissimilarity’ between two execution points?1. Consider solely total power difference

Conceals significant phase information2. Consider manhattan distance between the

corresponding 2 vectors Vectors with small magnitudes are inherently closer

3. Consider manhattan distance between the corresponding 2 vectors normalized Indifferent to magnitude of power consumption

4. Consider a combination of (2) & (3) Restricts us to both absolute and ratio-wise similarities

Construct a “similarity matrix” to represent similarity among all pairs of execution points

Each entry in the similarity matrix:

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Gcc Component Power Breakdowns

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att

s] L1 cache

TraceCache

RETIRE

Gcc Total Power

0

10

20

30

40

50

60

0 50 100 150 200 250

Po

wer

[W

atts

]

MEASURED POWER COUNTER ESTIMATED POWER

Gzip Total Power

0

10

20

30

40

50

60

44 88 132 176 220 264 308 352 396 440Time (s)

Pow

er [W

atts

]

MEASURED POWER COUNTER ESTIMATED POWER

Gzip Component Power Breakdowns

0

1

2

3

4

5

6

44 144 244 344 444Time (s)

Pow

er [W

atts

] L1 cache

INT Exec

RETIRE

Gcc

& G

zip

Mat

rix

Plo

tsG

cc &

Gzi

p M

atri

x P

lots

Gcc Elaboration: Very variant power

Almost identical power behavior at 30, 50, 180s.

Although 88s, 110s, 140s, 210s and 230s show similar total power; 88, 210 and 230 share higher similarity.

Gcc Total Measured Power

0

10

20

30

40

50

60

0 50 100 150 200 250Time (s)

Po

wer

[W

atts

]

Gzip Elaboration: Much regular power behavior

Spurious similarities such as 100-150s and 200-280 are distinguished by the similarity analysis

GZIP Total Measured Power

0

10

20

30

40

50

60

44 94 144 194 244 294 344 394 444

Time (s)

Po

wer

[W

atts

]

58

Similarity for Simplicity?Similarity for Simplicity? So, we can identify similar power phases:

I.e. informally: if similarity matrix(r,c) is DARK Execution points r & c have similar power behavior

2 Questions: 1) How do we group the execution points (power vectors) based on their similarity? 2) Could we represent power behavior with reasonable accuracy, with a small number of ‘signature’ vectors?

Our answer to Q.1: “Thresholding Algorithm”: Define a threshold of similarity < % of max dissimilarity> Start from first execution point (0,0) and identify ones in the fwd execution path that lie within threshold for both

normalized and absolute metrics Tag the corresponding execution points (j,j) as the same group Find next untagged execution point (r,r,) and do the same along fwd path Rule: A tagged execution point cannot add new elements to its group!

We demonstrate the outcome of thresholding with Grouping Matrices

59

Gzip Grouping MatricesGzip Grouping Matrices

Gzip has 974 power vectors Cluster vectors based on similarity

using “thresholding” Max Gzip power dissimilarity: 47.35W

60

ConclusionConclusion Presented a power oriented methodology to

identify program phases that uses power vectors generated during program runtime

Provided a similarity metric to quantify power behavior similarity of different execution samples

Demonstrated our representative sampling technique to characterize program power behavior Representative vectors for program power signatures Execution points for representative simulation

Can be useful for power & characterization research: Power Phase identification/prediction Reduced power simulation Dynamic power/thermal management