On the Scalability of Constraint Solving for Static/O ine ...On the Scalability of Constraint...
Transcript of On the Scalability of Constraint Solving for Static/O ine ...On the Scalability of Constraint...
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On the Scalability of Constraint Solving forStatic/Offline Real-Time Scheduling
R. Gorcitz1 E. Kofman2 D. Potop-Butucaru2
R. De Simone2 Thomas Carle3
1CNES, France
2INRIA, France
3Brown University, USA
December 1, 2015 - Syncron 2015 - Kiel
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Outline
Motivation
Contribution
Motivating example and encoding principles
Test cases
Solving and results
Conclusion
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Motivation
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Motivation
I Formal methods:I Many things can be done (qualitative point of view)I But which ones work in practice? (quantitative point of view)
I Particularly interesting question for NP-complete problemsI Exponential theoretical upper bound (cf. P 6=NP)I Many instances of NP-complete problems are rapidly solved.I Practical interest: SAT solving, some scheduling problems,
some compilation problems, etc.
I Empirical evaluation of the hardness is neededI Determine under which conditions NP-complete problems are
harder (or easier) to solve, on averageI Extensive evaluations for 3-SAT (and some other problems)
I E.g. 3-SAT problems are harder to solve when theclauses/variables ratio is 4.3 [Selman et al. 1993]
I Difficulty: each problem needs a separate evaluationI Different set of parameters to varyI Synthesis of meaningful test cases is application-dependent
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Motivation
I Formal methods:I Many things can be done (qualitative point of view)I But which ones work in practice? (quantitative point of view)
I Particularly interesting question for NP-complete problemsI Exponential theoretical upper bound (cf. P 6=NP)I Many instances of NP-complete problems are rapidly solved.I Practical interest: SAT solving, some scheduling problems,
some compilation problems, etc.
I Empirical evaluation of the hardness is neededI Determine under which conditions NP-complete problems are
harder (or easier) to solve, on averageI Extensive evaluations for 3-SAT (and some other problems)
I E.g. 3-SAT problems are harder to solve when theclauses/variables ratio is 4.3 [Selman et al. 1993]
I Difficulty: each problem needs a separate evaluationI Different set of parameters to varyI Synthesis of meaningful test cases is application-dependent
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Motivation
Figure: Y-chart design methodology
Allocating and scheduling tasks and messages over a network ofresources is NP-Complete (for most problem instances).
Two main approaches:
I Avoiding NP-Completeness: heuristics
I Accepting it: general solvers (Integer Linear Programming /SAT Modulo Theory / Constraint Programming)
I How far? (with “up to date” solvers)I Small size problems (can still be interesting)I Design for scalability (what problems scale better)
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Motivation
Figure: Y-chart design methodology
Allocating and scheduling tasks and messages over a network ofresources is NP-Complete (for most problem instances).
Two main approaches:
I Avoiding NP-Completeness: heuristics
I Accepting it: general solvers (Integer Linear Programming /SAT Modulo Theory / Constraint Programming)
I How far? (with “up to date” solvers)I Small size problems (can still be interesting)I Design for scalability (what problems scale better)
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Motivation
Figure: Y-chart design methodology
Allocating and scheduling tasks and messages over a network ofresources is NP-Complete (for most problem instances).
Two main approaches:
I Avoiding NP-Completeness: heuristics
I Accepting it: general solvers (Integer Linear Programming /SAT Modulo Theory / Constraint Programming)
I How far?
(with “up to date” solvers)I Small size problems (can still be interesting)I Design for scalability (what problems scale better)
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Motivation
Figure: Y-chart design methodology
Allocating and scheduling tasks and messages over a network ofresources is NP-Complete (for most problem instances).
Two main approaches:
I Avoiding NP-Completeness: heuristics
I Accepting it: general solvers (Integer Linear Programming /SAT Modulo Theory / Constraint Programming)
I How far? (with “up to date” solvers)I Small size problems (can still be interesting)I Design for scalability (what problems scale better)
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Contribution
I Encoding of a variety of off-line real-time scheduling problemsas SMT/ILP/CP problems
I Synthetic and realistic test cases (open, try them!)I A new task set generatorI Platooning, FFT
I Empirical hardness related to various parameters:I Problem sizeI PreemtivenessI Satisfiability vs optimization objectiveI System loadI DependenciesI PipeliningI Resources homogeneityI Single-/Multi-period.
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Motivating example and encoding principles
Control steering, speed (and image capture parameters) to followthe vehicle ahead.
Imagecapture
Detectioncorrection
DisplaySobel_V_0
Sobel_V_X
... Histo_V_0
Histo_V_X
...
Sobel_H_0
Sobel_H_X... Histo_H_0
Histo_H_X...
Figure: Platooning dataflow graph application
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Motivating example and encoding principles
IC DC
DSV HV
SH HH
Figure: Platooning dataflow graph application, no pipelining, nodata-parallelism (X=0) and simplified labels...
Encoding: ILP/SMT/CP set of linear and Boolean constraints.Application constraints:
I ICStop = ICStart + ICDuration
I SHStart ≥ ICStop
I SVStart ≥ ICStop
I ...
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Motivating example and encoding principles
IC DC
DSV HV
SH HH
Figure: Platooning dataflow graph application, no pipelining, nodata-parallelism (X=0) and simplified labels...
Encoding: ILP/SMT/CP set of linear and Boolean constraints.Application constraints:
I ICStop = ICStart + ICDuration
I SHStart ≥ ICStop
I SVStart ≥ ICStop
I ...
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Motivating example and encoding principles
IC DC
DSV HV
SH HH
Figure: Platooning dataflow graph application, no pipelining, nodata-parallelism (X=0) and simplified labels...
Encoding: ILP/SMT/CP set of linear and Boolean constraints.Application constraints:
I ICStop = ICStart + ICDuration
I SHStart ≥ ICStop
I SVStart ≥ ICStop
I ...
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Motivating example and encoding principles
IC DC
DSV HV
SH HH
Figure: Platooning dataflow graph application, no pipelining, nodata-parallelism (X=0) and simplified labels...
Encoding: ILP/SMT/CP set of linear and Boolean constraints.Application constraints:
I ICStop = ICStart + ICDuration
I SHStart ≥ ICStop
I SVStart ≥ ICStop
I ...
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Motivating example and encoding principles
IC DC
DSV HV
SH HH
P2 P3
P1
Figure: Platooning dataflow graph application single period, nodata-parallelism (X=0) and simplified labels...
Resource constraints:
I ICMap ∈ {P1,P2,P3},SHMap ∈ {P1,P2,P3}, ...if SHMap = SVMap then
SHStart ≥ SVStop or SHStop ≥ SVStart
I ...
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Motivating example and encoding principles
IC DC
DSV HV
SH HH
P2 P3
P1
Figure: Platooning dataflow graph application single period, nodata-parallelism (X=0) and simplified labels...
Resource constraints:
I ICMap ∈ {P1,P2,P3}, SHMap ∈ {P1,P2,P3}, ...
if SHMap = SVMap thenSHStart ≥ SVStop or SHStop ≥ SVStart
I ...
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Motivating example and encoding principles
IC DC
DSV HV
SH HH
P2 P3
P1
Figure: Platooning dataflow graph application single period, nodata-parallelism (X=0) and simplified labels...
Resource constraints:
I ICMap ∈ {P1,P2,P3}, SHMap ∈ {P1,P2,P3}, ...if SHMap = SVMap then
SHStart ≥ SVStop or SHStop ≥ SVStart
I ...
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Motivating example and encoding principles
Realistic modeling (in plain English):
1. Each task is allocated on exactly one processor
2. If two tasks are ordered, the second starts after the first ends
3. If two tasks are allocated on the same processor, they must beordered
4. The source and destination of a dependency must be ordered5. The bus communication associated with a dependency (if any) must
start after the source task ends and must end before the destinationtask starts
6. When two dependencies require both a bus communication, thesecommunications must be ordered
7. If two dependencies are ordered, the first must end before the second starts8. All tasks must end at a date smaller or equal than the schedule length
9. ...
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Motivating example and encoding principles
Encoding more complex scheduling problems (added complexity):I Pipelined scheduling
I Modulo scheduling, non-linear (modulo) operators
I Preemptive schedulingI Each task must be statically divided into atomic chunks
I Multi-period schedulingI Strict periodicity vs. Non-strictI Number of start date variablesI Implicitly pipelined
I Heterogenous vs. homogenous schedulingI Symmetry arguments, etc.
I Not done: Accounting for preemption costs, migrations, etc.
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Motivating example and encoding principles
Result in the simple case:
P1
Bus
P2
0 0.005 0.01 0.015 0.02 0.025 0.03
ICIC
->SV
HHHV
HV->
DC
SV
DSH
SV->
DDC
time
Figure: Gantt diagram (optimal non-preemptive, non-pipelined schedule)
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Test cases
Synthetic examples:
I Dependent task systems (algorithm of [Carle and Potop 2014])I Non-dependent task systems
I Classical synthesis algorithms cannot be used to provide abasis of comparison E.g. UUniFast of [Bini&Buttazzo 2005]cannot handle heterogenous architectures
I For each problem type:I Number of tasks n ranging from 7 to 147 with step 5I Number of processors ceil(n/5)I 40/25 problem instances for each problem and sizeI Periods chosen randomly in the set 5,10,15,20,30,60I Average system loads: 25%, 75%, 87.5%,125%I WCETs generated accordinglyI Allocation: 30% probability of fixed allocation (random CPU).
For open allocation: 70% probability that a task is allocatedto a given processor (heterogenous case).
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Test cases
Realistic examples: FFT, Platooning (we need more!)
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Test cases
Parameters that we tune:
I Problem type: schedulability vs. optimizationI Task set properties:
I PreemtivenessI Dependent vs. Non-dependentI Single-/Multi-periodI PipeliningI Resource homogeneity
I Problem size
I Average system load (25%, 75%, 87.5%,125%)
I Pipelining depth (for the platooning example)
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Solving and results
We look at:
I Average solving time
I Timeout (1 hour) ratio
Figure: Timeout (1h) rate (preemptive, multi-periodic, heterogenous,75% load)
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Solving and results
Figure: Single Period Non-Preemptive vs Multi Period Preemptive
→ Preemptive problems are more complex→ But non-preemptive scheduling has its bounds, too.
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Solving and results
Figure: Homogeneous resources VS Heterogeneous resources
→ Not so clear
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Solving and results
Figure: Complexity as a function of system load (single-period,non-preemptive, heterogenous)
→ Underloaded and overloaded systems are easier to handle.
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Solving and results
Figure: Optimization vs. Schedulability analysis (single-period,non-preemptive, heterogenous, 75% load)
→ Optimization problems are extremely difficult
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Solving and results
The dependent task synthetic examples:
I Removing dependencies always reduces solving time (intuitiveexplanation: having a smaller search space is less importantthan its complexity)
I Timeout rate:I dependent task systems: 55%I after dependency removal: 13%
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Solving and results
The FFT example results (non-preemptive, single-period,dependent):
Figure: Pipelining: Cyclic dataflow graph to DAG
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Solving and results
Consider 2 types of parallelism (separately):
I Pipelining.
I Data parallelism (split/merge).
Figure: Pipelining: Cyclic dataflow graph to DAG
Pipelining raises the depth of the graph
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Solving and results
SobelH04
HistoH04
SobelH03
HistoH03
SobelH01
HistoH01
SobelV01
HistoV01
Display01
SobelV04
HistoV04
Display04
SobelV03
HistoV03
Display03
Detection01
Detection04
Read01
Read02
Detection03
HistoH02
Detection02
HistoV05
Detection05
HistoH00
Detection00
Read03
Read04
Display00
SobelH02
SobelH00
SobelV05
Display05
SobelV00
HistoV00
SobelV02
HistoV02
Display02
Read05
SobelH05
HistoH05
Read00
SobelV2
Display0
HistoV2 HistoV1
Detection0
SobelH5
HistoH5
SobelV3
HistoV3 HistoV4
Read0
SobelH4 SobelH3SobelV5 SobelH2 SobelH1 SobelV1SobelH0 SobelV0 SobelV4
HistoV0HistoH2 HistoH4 HistoH3HistoV5 HistoH1HistoH0
Figure: Pipelining vs. data parallelism
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Solving and results
Split/merge parallelism that allows the application of symmetrybreaking techniques (homogenous architecture) !
Figure: Symmetry breaking helps on large graphs
Exponential curve, even after symmetry breaking.
Deep graphs will scale much better than large graphs (the deepgraph scales up to 100 tasks in our case).
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Solving and results
Split/merge parallelism that allows the application of symmetrybreaking techniques (homogenous architecture) !
Figure: Symmetry breaking helps on large graphs
Exponential curve, even after symmetry breaking.Deep graphs will scale much better than large graphs (the deepgraph scales up to 100 tasks in our case).
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Conclusion
Good practices for encoding scheduling problems:
I Certain problems (especially parallelized code) can exhibit alot of symmetry. It is imperative to use symmetry-breakingtechniques.
I Certain architectures (e.g. many-cores) exhibit a lot ofsymmetry.
I The more complex is to specify, the more complex is to solve.
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Conclusion
Thank you
Questions ?
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Conclusion
Thank you
Questions ?
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