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Bounding Variance and Expectation of Longest Path Lengths in DAGs Jeff Edmonds, York University

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Bounding Variance and Expectation of Longest Path Lengths in DAGs Jeff Edmonds, York University Supratik Chakraborty, IIT Bombay. Motivation. Statistical timing analysis of circuits Mean and std deviation of component delays provided by manufacturers

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slide1
Bounding Variance and Expectation of Longest Path Lengths in DAGs

Jeff Edmonds, York University

Supratik Chakraborty, IIT Bombay

motivation
Motivation

Statistical timing analysis of circuits

Mean and std deviation of component delays provided by manufacturers

Joint distributions of component delays difficult to obtain in practice

the longest path problem
The Longest Path Problem

Input:

st-DAG G gives job precedence.

For each edge i, xi is the time to complete job i

Output:

Time for all jobs to complete in parallel = length of longest st-path = Maxp i p xi = XG

s

x1

x3

x2

t

Easy with Dynamic Programming

the longest path problem1
The Longest Path Problem

Input:

st-DAG G gives job precedence.

For each edge i, xi is the time to complete job i

Output:

Time for all jobs to complete in parallel = length of longest st-path = Maxp i p xi = XG

s

Inter-dependent random variables

x1

x3

x2

t

Understand random variable XG

the longest path problem2
The Longest Path Problem

Input:

st-DAG G gives job precedence.

For each edge i, xi is the time to complete job i

Output:

Time for all jobs to complete in parallel = length of longest st-path = Maxp i p xi = XG

s

Exp[Xi] & Var[Xi]

x1

x3

x2

t

Bound Exp[XG] & Var[XG]

the longest path problem3
The Longest Path Problem

Input:

XG = Max( x1+x2, x3 )

4

5

0

1

s

  • Possible distributions :

x1

x3

x2

t

  • Another possibility :
the longest path problem4
The Longest Path Problem

Input:

XG = Max( x1+x2, x3 )

Upper & Lower bounds

s

x1

x3

x2

t

contributions
Contributions

Upper bounds of Exp[XG] and Var[XG]

A spring “algorithm” for computing bounds

Proof no distributions give higher values (skip)

Cake distributions that achieve bounds

Lower bounds of Exp[XG] and Var[XG]

Continuum of values for Exp[XG] and Var[XG]

Cake distributions that achieve any Exp[XG] and Var[XG] within range

Special results for series-parallel graphs

series graphs
Series Graphs

If G is a series graph,

XG = ∑i xi

Exp[xG] = ∑i Exp[xi]

0 ≤ Var[xG] ≤ (∑i √Var[xi] )2

s

t

series graphs1
Series Graphs

If G is a parallel graph,

XG = Maxi xi

Maxi Exp[xi] ≤ Exp[xG] ≤ ?

0 ≤ Var[xG] ≤ ?

s

t

representing random variables
Representing Random Variables

X

5

0

r

0

0.5

1

X : Two-valued random variable, prob 0.5 for each value

representing random variables1
Representing Random Variables

X

5

Z

0

r

0

0.5

1

X, Z : Two equivalent independent random variables.

representing random variables2
Representing Random Variables

X

Y

5

0

r

0

0.5

1

X, Y : Two-valued random variables, prob 0.5 for each value

X, Y have perfect negative correlation

Exp( Max(x,y) ) = Exp(x) + Exp(y)

Var( Max(x,y) ) = 0

series graphs2
Series Graphs

If G is a parallel graph,

XG = Maxi xi

Maxi Exp[xi]

≤ Exp[xG]

≤ Min(

∑i Exp[xi],

Maxi Exp[xi] + √∑i Var[xi] )

0 ≤ Var[xG] ≤ ∑i Var[xi]

s

t

series parallel graphs
Series Parallel Graphs

Theorem

In a series-parallel graph,

Rules for maximum variance applied recursively to obtain Max Var[XG].

Not so Max Exp[XG]

maximizing var x g
Maximizing Var [ XG ]

There are no distributions xi for which

Var[xi] = vi and Exp[xi] = mi

Var[XG] >

Proof uses lots of calculus.

Theorem

cakes maximizing var x g
Cakes Maximizing Var [ XG ]

There exists “cake” distributions xi such that

Var[xi] = vi and Exp[xi] = mi

Var[XG] =

Theorem

cake distribution
Cake Distribution

s

t

Find a cake distribution for each edgewith correct Exp[xi] & Var[xi]

to maximize Var[xG]

cake distribution2
Cake Distribution

s

t

Var[xi] = ∑c (ε hc)2

cake distribution3
Cake Distribution

s

t

  • Series graphs G:
  • XG ≈ x1 + x2
  • Candle heights add
  • Want candle heights to be in same location
cake distribution4
Cake Distribution

s

t

  • Parallel graphs G:
  • XG ≈ Max( x1 , x2 )
  • Candle heights max
  • Want candle heights to be in different location
cake distribution5
Cake Distribution

s

t

A candle location

for each st-path in G

but in the end

# candles ≈ # edges

cake distribution6
Cake Distribution

s

t

If edge i not in path p,

candle for xi at location p has height 0

If candle is selected,

then corresponding path pis the longest path

cake distribution7
Cake Distribution

s

t

“Springs” give

give candle heights.

cakes maximizing var x g1
Cakes Maximizing Var [ XG ]

There exists “cake” distributions xi such that

Var[xi] = vi and Exp[xi] = mi

Var[XG] =

Theorem

Proved

lower bound of var x g
Lower Bound of Var[ XG ]

TheoremVar[xG] ≥ 0

Continuum Results

Theorem

Every Var[XG] in this range achievable.

upper bound of exp x g
Upper bound of Exp [ XG ]

XG

r

tp

0

1

For st-path p, tp is interval for which p is the longest path.

 p P tp = 1

upper bound of exp x g1
Upper bound of Exp [ XG ]

XG

r

tp

0

1

ti

For edge i, ti is interval for which i is in the longest path.

ti =  p itp

upper bound of exp x g2
Upper bound of Exp [ XG ]

Xi

r

tp

0

1

ti

If it can edge i contributes all of its mi =Exp[Xi] to Exp[XG]

upper bound of exp x g3
Upper bound of Exp [ XG ]

Xi

r

tp

0

1

ti

But if vi = Var[Xi] is too small,

it can only contribute

conclusion future work
Conclusion & Future Work

Tight analysis for upper bounds was achieved

Cake distributions particularly important for achieving tight bounds

A related question is that of finding tight bounds of mean and expectation of difference in longest paths to two given nodes in a DAG

Spring algorithm involves solving non-linear constraints iteratively. Can an alternative algorithm be obtained?

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