Modeling and Optimization of Scientific Workflows - Google Sites

Modeling and Optimization of Scientific Workflows Daniel Zinn Department of Computer Science University of California at Davis

March 25th, 2008

Daniel Zinn

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University of California, Davis

Scientific Workflows

C OMAD

Research Questions

Analysis & Optimization

Experiments

Conclusion

Outline

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2

Collection-Oriented Modeling and Design (C OMAD)

3

Research Questions

4

Dataflow Analysis and Optimization

5

Experimental Evaluation

6

Conclusion

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C OMAD

Research Questions


Experiments

Conclusion

Outline

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Research Questions

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5


6

Conclusion

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C OMAD

Research Questions


Experiments

Conclusion

Natural Sciences and E-Science Research Earth Sciences

dataintensive

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Physical Sciences

Life Sciences

computeintensive structurally & semantics metadataintensive intensive

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C OMAD

Research Questions


Experiments

Conclusion

Workflows: An Introduction Workflow Basics Dataflow network represented as graph Actor: represents computational unit (legacy components) Channel: represents dataflow between these units Types on the Channels Form of visual programing

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C OMAD

Research Questions


Experiments

Conclusion

Scientific Workflow Modeling & Design

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C OMAD

Research Questions


Experiments

Conclusion

Sometimes: Brittle and Ugly

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C OMAD

Research Questions


Experiments

Conclusion

Desiderata

Scientific Workflow Systems should ... (1) support high-level design and maintenance of workflows and data (save scientists’ brain cycles) (2) support automatic optimization of scientific workflows on parallel/distributed systems (save machine cycles)

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C OMAD

Research Questions


Experiments

Conclusion

Outline

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Research Questions

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6

Conclusion

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C OMAD

Research Questions


Experiments

Conclusion

Collection-Oriented Workflows [McPhillips2005] C OMAD — adopting assembly-line metaphor Data is organized in nested collections Actors “pick up” only relevant data (read scope) and put results back Actors ignore (pass through) what’s outside the scope

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C OMAD

Research Questions


Experiments

Conclusion


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C OMAD

Research Questions


Experiments

Conclusion


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C OMAD

Research Questions


Experiments

Conclusion


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C OMAD

Research Questions


Experiments

Conclusion

C OMAD vs. Conventional Workflows Advantages∗ Mostly linear WF design (easier for the scientist to understand) Easier to reuse (change-resilience: usually can add, remove, swap-out actors w/o breaking the pipeline) More robust to input changes Can be automatically optimized

∗

Fineprint: Complexity moved to configuration layer

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C OMAD

Research Questions


Experiments

Conclusion

Resilience to Input Changes Input

α

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Conventional Workflow α

β A

γ B

12

Collection-Oriented Workflow α

β

γ

A

B

α→β

β→γ



C OMAD

Research Questions


Experiments

Conclusion


α [α]


β

[β]

[γ]

*

* β

A

α

B

[α] α

Daniel Zinn

γ

A

Collection-Oriented Workflow

β

γ

β

γ

A

B

α→β

β→γ

[α]

[β]

[γ]

A

B

α→β

β→γ

B

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C OMAD

Research Questions


Experiments

Conclusion


α [α]


β

γ

A

[β]

[γ]

* β

[α|ϕ]

[β|ϕ]

β|ϕ γ|ϕ

S

α→β

β→γ [β]

[γ]

A

B

α→β

β→γ

[α | ϕ]

[γ | ϕ]

[β | ϕ] A

B

α→β

β→γ

S β

A

[γ|ϕ] *

β|ϕ

α

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γ

γ B

B

* α|ϕ

β A

[α]

* β

A

[α | ϕ]

α

B

[α] α

Collection-Oriented Workflow

γ

β B

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C OMAD

Research Questions


Experiments

Conclusion

Three-Layer Architecture Count Alignments

Data A

Sink

Workflow Graph Data B

Configurations (White Box Part) Scientific Functions

Update Statistics

Merge

Align DNA Sequences [ClustalW]

Refine Alignment [Gblocks]

s : DNASeq+ → append f(s)

DNASeq+

f

Infer Set of PhylTrees [DNAPARS]

Compute a Consensus Tree [CONSENSE]

Display DNASequences, Infered Tree

a : Alignment → append PhylTrees[q(a)]

Alignment

Alignment

q

PhylTree+

(Black Box Part)

Graph as clean representation of scientific process White-box layer for data-management Legacy/scientific functions as black boxes Analyze and optimize based on white-box layer Daniel Zinn

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C OMAD

Research Questions


Experiments

Conclusion

Outline

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Research Questions

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Conclusion

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C OMAD

Research Questions


Experiments

Conclusion

Research Questions In a nutshell Define a good white-box layer ... with concrete language, with solid theoretical basis, with appropriate type system ... and show how workflow desiderata can be achieved

Desiderata Support workflow design! Support workflow optimization!

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C OMAD

Research Questions


Experiments

Conclusion

Specific Research Questions Resilience and robustness Characterize input schema changes not effecting the workflow Characterize actor addition/removal/replacement not effecting the workflow Modeling support Infer output schema Check if all actors are active Infer canonical input schema Automatic optimization Reduce shippings Detect and exploit parallelism Daniel Zinn

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C OMAD

Research Questions


Experiments

Conclusion


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C OMAD

Research Questions


Experiments

Conclusion


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C OMAD

Research Questions


Experiments

Conclusion

Outline

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2


3

Research Questions

4


5


6

Conclusion

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C OMAD

Research Questions


Experiments

Conclusion

Results Adopted type-system for the channels Tree-grammars to describe schema on channels (XML-schema) Type-level signatures for actors Type propagation through actors Shipping optimization Based on dependency analysis Reduces amount of data shipped Improved execution time

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C OMAD

Research Questions


Experiments

Conclusion

Typing in C OMAD Conventional Actor τ0

τ

−→ A : α → ω −→ C OMAD- Actor

τ0

τ

−→ ∆A : τα → τω −→

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C OMAD

Research Questions


Experiments

Conclusion

Typing in C OMAD Conventional Actor τ0

τ

−→ A : α → ω −→ C OMAD- Actor

τ0

τ

−→ ∆A : τα → τω −→ context paths

!A : #" " #!

"matched" fragments

#" Daniel Zinn

"replaced" fragments

A

#

#"

#! 19

#'

#! University of California, Davis


C OMAD

Research Questions


Experiments

Conclusion

Channel Type System Definitions Type declaration: T ::= hti R

τ: S hsi

hai

hbi

A ::= hai D + | E ∗

D+ | E ∗ C hdi

Schema τ : set of type declarations with induced labels Lτ , and types Tτ = Cτ ∪˙ {Z }

Tτ = {S, A, . . . , Z } Lτ = {hsi, hai, . . . , hhi}

(A | B)∗

hei

F ∗G hfihgi

Z Z

hci

H∗

Z

Restrictions and conventions Non-ambiguous, non-recursive tree grammars

hhi

Z

s ∈ Jτ K: s = s[b[c]a[d[ffg]d[gg]]a[e[hhh]]b[c]b[c]]

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Leaf nodes hold the actual data (Z )

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C OMAD

Research Questions


Experiments

Conclusion

Actor Type-level Signatures context paths

!A : #" " #!

"matched" fragments

"replaced" fragments

A

#"

#

#"

#!

#'

#!

Formal Signature ∆A = hσ, τα , τω i "1

!1 "2

!2 "3

!3

A1 A2 A3 τα(a) – input selection schema τω – new output schema parts σ – set of form X → !31 "4 "1 match rules, !1 "2 each of!the !3 R with 2 "3 A1 (b) X ∈ τ , and α

A2

A3

F

A4

R regexp. over types in τα ∪ τω Daniel Zinn

"1

!1 "2

21

!2

!22

"3 of California, !3Davis University


C OMAD

Research Questions


Experiments

Conclusion

Example τ

τ0

S

S

(A | B)∗

(A | B)∗ C

C

D+ | E ∗ (BEG∗ )∗ G C

D+ | E ∗ H∗

H∗

(BE(E |

H∗

C

H∗

B)∗ )∗ (E

H∗ C

| B)

H∗ C

A ∆A = hσ, τα , τω i with σ : {G → E | B}, τα : {G ::= hgi Z }, and τω : {E ::= hci H ∗ , B ::= hbi C, H ::= hhiZ , C ::= hciZ }. Daniel Zinn

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C OMAD

Research Questions


Experiments

Conclusion

Initial Contributions

S

S (A |

B)∗

(A |

S B)∗

i∈τ

(BEG∗ )∗ G

H∗

C

A1 σA1 = {F → BEG∗ }

C

D+ | E ∗

D+ | E ∗

D+ | E ∗

(A | B)∗ C

C

C

F ∗G

(A |

S B)∗

D+ | X ∗ H∗

H∗

(BE(E | B)∗ )∗ (E | B)

H∗

C

H∗

H∗ C

(BX ∗ (X | B)∗ )∗ (X ∗ | B)

H∗ C

A2 σA2 = {G → (E | B)}

C

A3 σA3 = {E → X }

C

C

o

Type propagation (WF design support!) Detect non-active actors (WF design support!) Shipping optimization (WF optimization!)

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C OMAD

Research Questions


Experiments

Conclusion

Workflow Example: Shipping optimized (part)

τ

τ

S hsi

(A + B)∗ hai

D+E ∗ hdi

F ∗G

Daniel Zinn

hbi

D0

◦ d01 • d02

S

A1

(A | ◦04 )∗ •04 D + | ◦∗03

• d03

C ◦ d01

hei

H∗

• d04

E

C

•02 • d02

24

B

•03

F ∗ ◦02

G

H∗

• d04

• d03



C OMAD

Research Questions


Experiments

Conclusion

Workflow Example: Shipping optimized (complete) S

S

S

S S

S (A | ◦04 )∗

(A | ◦04 )∗

(A | ◦04 D + | ◦∗03

D + | ◦∗03

F ∗ ◦02

(BEG∗ )∗ ◦02 C

i∈τ

D0

• d02

D + | ◦∗03

A1

o1

H∗

◦ d12

D1

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A2

o2

D2

H∗

(◦14 X ∗ (X | ◦24 )∗ )∗ (X ∗ | ◦24 )

H∗

◦ d23

M3

i3

A3

o3

M4

o

• d24

S

S

S

(A | ◦04 )∗

(A | ◦04 )∗

(A + B)∗

hfihgi

i2

H∗

• d14

hsi

F ∗G

M2

H∗ C

C

D+ | X ∗

H∗ (◦14 E(E | ◦24 )∗ )∗ (E | ◦24 )

(◦14 ◦13 (E | B)∗ )∗ (E | B)

• d13

S

hdi

D+ | E ∗

(A | ◦04 )∗

• d04

D+E ∗

D + | ◦∗03

)∗

• d03

hai

(A | ◦04 )∗

(◦14 ◦13 G∗ )∗ G

H∗

◦ d01 = i1

(A | ◦04 )∗

(A | ◦04 )∗

•04 D + | ◦∗03

hbi

C hei

◦ d01

hci

H∗ hhi

F ∗ ◦02

◦ d12

B

•03 E

C

•02

• d02

G

H∗

• d04

• d03 • d14

D + | ◦∗03

D + | ◦∗03

(◦14 ◦13 G∗ )∗ ◦02

(◦14 ◦13 (E | ◦24 )∗ )∗ (E | ◦24 )

•14

•13

B

E

C

H∗

25

◦ d23

• d13

H∗

• d24

•24

H∗

•24

B

B

C

C

• d24



C OMAD

Research Questions


Experiments

Conclusion

Shipping Savings

Shipping optimality There is no unnecessary base data shipping∗ ∗

of course, signature might be too coarse, etc.

O( shipping savings )? Savings are linear in bypassed data size Savings are linear in number of bypassed actors

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C OMAD

Research Questions


Experiments

Conclusion

Outline

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3

Research Questions

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6

Conclusion

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C OMAD

Research Questions


Experiments

Conclusion

Experimental Evaluation Experimental Environment Cluster of 40 Linux nodes 100MBit/s networked Shared nothing architecture Parallel Workflow System’s Specs Workflow system written in C++/Perl PVM as distribution “library” Each actor as single process PVM for passing XML tokens Data as local files; scp for passing data tokens

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C OMAD

Research Questions


Experiments

Conclusion

Experimental Setup

Sample Workflow Chosen to study impact of data-shipping savings alone! Data-intensive but not a CPU-intensive pipeline A1

A2

A3

∆A1 : σ = {A → B | U} ∆A2 : σ = {B → C | V } ∆A3 : σ = {C → W }.

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C OMAD

Research Questions


Experiments

Conclusion

One Workflow — Different Behaviors

Scenario

(a) Parallel

(b) Serial (c) Mixed

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A1 : hai 7→ hui A2 : hbi 7→ hvi A3 : hci → 7 hwi A1 : hai 7→ hbi A2 : hbi 7→ hci A3 : hci 7→ hwi A1 : hai 7→ hbi A2 : hbi 7→ hvi A3 : hci 7→ hwi

Input Data Input Workflow

Actual Dataflow A1

s[ (a[z] b[z] c[z] w[z]) ∗ i ] A1

A2

A2

D0

M4

A3

A3

s[ a[z] ∗ i ] A1

A2

A3

D0

s[ (a[z] ∗ i) (c[z] ∗ i) ] A1

A2

30

A3

D0

A1

A2

A3

A2

A1 A3

M4

M4



C OMAD

Research Questions


Experiments

Conclusion

Experimental Analysis Input Data Input Workflow

Scenario A1 : hai 7→ hui A2 : hbi 7→ hvi A3 : hci → 7 hwi

(a) Parallel

(c) Mixed

opt.

A2

D0

M4

A3

80i

35i −56%

≈ 3.6i

≈ 1.1i −69%

80i

80i 0%

≈ 3.6i

≈ 2.6i −28%

80i

50i −38%

≈ 3.6i

≈ 2.2i −39%

A3

A1 : hai 7→ hbi A2 : hbi 7→ hci A3 : hci 7→ hwi A1 : hai 7→ hbi A2 : hbi 7→ hvi A3 : hci 7→ hwi

(b) Serial

A2

Exec. Time (sec) orig. opt.

orig.

A1

s[ (a[z] b[z] c[z] w[z]) ∗ i ] A1

Data Shipped (MB)

Actual Dataflow

s[ a[z] ∗ i ] A2

A1

D0

A3

A1

s[ (a[z] ∗ i) (c[z] ∗ i) ] A2

A1

M4

A3

A2

A1

D0

A3

A2

A3

M4

Runtime Measurements 80 70 60 50 40 30 20 10 0

80 70 60 50 40 30 20 10 0

Orig individual Orig avgerage Opt individual Opt average

0

2

4

6

8

10

12

Parallel Daniel Zinn

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16

18

20

80 70 60 50 40 30 20 10 0

Orig individual Orig average Opt individual Opt average

0

2

4

6

8

10

12

Serial 31

14

16

18

20

Orig individual Orig average Opt individual Opt average

0

2

4

6

8

10

12

14

16

18

20

Mixed University of California, Davis


C OMAD

Research Questions


Experiments

Conclusion

Outline

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2


3

Research Questions

4


5


6

Conclusion

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C OMAD

Research Questions


Experiments

Conclusion

Conclusion Scientific Workflows & C OMAD C OMAD: a model of computation with many advantages Resilience to input changes Supporting workflow evolution Powerful configuration layer needed Contributions Expressive type-system Defined actor signatures Algorithm for type propagation Analysis of actor dependency Dataflow optimization Daniel Zinn

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Bibliography A. Brüggemann-Klein, M. Murata, and D. Wood. Regular tree and regular hedge languages over unranked alphabets. Unpublished, 2001. H. Comon, M. Dauchet, R. Gilleron, F. Jacquemard, D. Lugiez, S. Tison, and M. Tommasi. Tree automata techniques and applications. http://www.grappa.univ-lille3.fr/tata, 1997. Haruo Hosoya, Jerome Vouillon, and Benjamin C. Pierce. Regular expression types for xml. ACM Transactions on Programming Languages and Systems (TOPLAS), 2005. Edward A. Lee and Thomas Parks. Dataflow process networks. Proceedings of the IEEE, 83(5):773–799, May 1995. Timothy M. McPhillips and Shawn Bowers. An approach for pipelining nested collections in scientific workflows. SIGMOD Record, 34(3):12–17, 2005.

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Thank you.

Questions? Daniel Zinn

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