sequential and parallel algorithms for some problems on trees raymond greenlaw 1 sequential and...

Sequential and Parallel Algorithms for Some Problems on TreesRaymond Greenlaw

Sequential and Parallel Algorithms for Some Problems on Trees

Raymond GreenlawArmstrong Atlantic State University

Sequential and Parallel Algorithms for Some Problems on Trees

Raymond GreenlawArmstrong Atlantic State University

Joint work with:P. de la Torre

Department of Computer ScienceUniversity of New Hampshire

M. Halldorsson Decode Genetics

T. Przytycka Department of Biophysics

John Hopkins University Medical School

A. SchafferNational Institute of Health

R. Petreschi Department of Computer ScienceUniversity of Rome La Sapienza

OutlineOutline

• Introduction• Parallel Preliminaries• Node Ranking• Edge Ranking• Prüfer Codes• Open Problems

OutlineOutline

TreesTrees

OutlineOutline

Parallel Random Access MachineParallel Random Access Machine

RAM Processors

Global Memory Cells

P0 P1 P2

C0 C1 C2

Preliminary Parallel AlgorithmsPreliminary Parallel Algorithms

• Brent’s Scheduling Principle• Parallel Prefix Computation• Euler Tour• List Ranking• Parallel Tree Contraction

Brent’s Scheduling PrincipleBrent’s Scheduling Principle

(Brent) If processor allocation is not a problemthen a t(n) time parallel algorithm that requires w(n) computational operations

canbe simulated using w(n)/p(n) + t(n) time

andp(n) processors.

Example of Brent’sExample of Brent’s

Parallel Prefix ComputationParallel Prefix Computation

(Ladner & Fisher)The Parallel Prefix Problem can besolved in O(log n) time using n/log nprocessors on an EREW-PRAM.

Example of Parallel PrefixExample of Parallel Prefix

Euler Tour Euler Tour

(Tarjan & Vishkin)An Euler tour of an n-node tree can becomputed in O(log n) time using n/log nprocessors on an EREW-PRAM.

Example of Euler TourExample of Euler Tour

List RankingList Ranking

(Anderson & Miller)Given a list with n nodes, the List Ranking Problem can be solved in O(log

n)time using n/log n processors on an EREW-PRAM.

Example of List RankingExample of List Ranking

Parallel Tree ContractionParallel Tree Contraction

(He; Miller & Teng; Abrahamson, Dadoun, Kirkpatrick & Przytycka)

Let T be an n-leaf regular binary expressiontree. Then all of the algebraic expressionsassociated with the internal nodes (one pernode) of T can be evaluated in O(log n) time using n/log n processors on an EREW-PRAM.

Example of Tree ContractionExample of Tree Contraction

Scrunched tree

OutlineOutline

Node RankingNode Ranking

• A node ranking is a labeling of the nodes of a tree with natural numbers such that if nodes u and v have the same label then there exists another node with a greater label on the path between them.

• An optimal node ranking is a node ranking in which the largest label assigned to any node is as small as possible among all node rankings.

• The node ranking problem is to compute an optimal node ranking.

Node Ranking ExampleNode Ranking Example

Node Ranking: Sequential ResultsNode Ranking: Sequential Results

• Node Ranking Problem• O(n log n) time (Iyer, Ratliff & Vijayan)• O(n) time (Schaffer)

Example of Sequential Node Ranking Algorithm

• Critical list at * is {3,4}• Label 3 at ** covers values 1 and 2

Example of Sequential Node Ranking Algorithm

• Critical list at * is {3,4}• Label 3 at ** covers values 1 and 2

Node Ranking: Parallel ResultsNode Ranking: Parallel Results

• Approximate Optimal Tree Ranking• O(log2 n) time, n processors EREW-PRAM (Liang, Dhall & Lakshmivarahan)• Optimal Tree Ranking• O(log n) time, n2/log n procs CREW-PRAM (de la Torre & Greenlaw) (Przytycka)• Super Critical Tree Numbering O(log n) time, n2/log n procs CREW-PRAM (de la Torre, Greenlaw & Przytycka)

OutlineOutline

Edge RankingEdge Ranking

• An edge ranking is a labeling of the edges of a tree with natural numbers such that if edges u and v have the same label then there exists another edge with a greater label on the path between them.

• An optimal edge ranking is an edge ranking in which the largest label assigned to any edge is as small as possible among all edge rankings.

• The edge ranking problem is to compute an optimal edge ranking.

Edge Ranking ExampleEdge Ranking Example

Edge Ranking: Sequential ResultsEdge Ranking: Sequential Results

• Approximate Edge Ranking• O(n log n) time (Iyer, Ratliff & Vijayan)• Optimal Edge Ranking• O(n3 log n) time (de la Torre, Greenlaw & Schaffer)• O(n2 log n) time (Zhou, Kashem & Nishizeki)• O(n log n) time, O(n) time (Lam & Ling)

From Local to Global OptimalityFrom Local to Global Optimality

Greedy Cover LabelingGreedy Cover Labeling

• Lc > Lc-1 > … > L1

is a greedy cover labeling if and only if cover(Li, crit(vj)) lex cover(Li, crit(vi))

for all i and for all j < i.

Example of Edge Ranking AlgorithmExample of Edge Ranking Algorithm

Edge Ranking: Parallel ResultsEdge Ranking: Parallel Results

• Approximate Edge Ranking• O(log2 n) time, n2/log n processors CREW-PRAM (de la Torre, Greenlaw & Schaffer)• Optimal Edge Ranking of Constant Degree Trees NC (de la Torre, Greenlaw & Schaffer)

Motivation for Studying RankingsMotivation for Studying Rankings

OutlineOutline

Definition of Prüfer CodeDefinition of Prüfer Code

A Prüfer code of a labeled free tree with n nodes is a sequence of length n – 2 constructed by the following sequential process:

for i ranging from 1 to n – 2 do insert the label of the neighbor of the smallest remaining leaf into the i-th

position of the sequence; delete the leaf;

Example of a Prüfer CodeExample of a Prüfer Code

(9,6,5,6,1,6,1,1)

Related WorkRelated Work

• Prüfer code to tree EREW-PRAM O(log n) time, n processors

proposed as an open problem the reverse direction

(Wang, Chen & Liu)• Tree to Prüfer code EREW-PRAM O(log n) time, n processors (Chen & Wang, Greenlaw & Petreschi)

Related Work ContinuedRelated Work Continued

• Chain to Prüfer code EREW-PRAM O(log n) time, n/log n (Greenlaw & Petreschi)• Used Prüfer codes for random tree generation (Kumar, Deo & Kumar)

Can Sequential AlgorithmPrüfer Code Be Parallelized?Can Sequential Algorithm

Prüfer Code Be Parallelized?

• Initial intuition suggests no.• What type of running time do we want?• How do we proceed?

Parallel Algorithm Prüfer ChainParallel Algorithm Prüfer Chain

The Prüfer code of this chain is(2,8,3,7,5,6)

Parallel Algorithm Prüfer ChainStep 1:

/* Compute the position of each node in the chain. */1. Use parallel list ranking to construct the

array Position such that Position[i] = v, 1 i n, where node v has a ranking of i in T

12865734

87654321

/* Compute the maximum nodes encountered thus far in left-to-right and right-to-left traversals over the chain. */

2. Use parallel prefix computation to construct the arrays LRMax and RLMax

LRMax[i] = max{Position[j] | 1 j i} RLMax[i] = max{Position[j] | n – i + 1 j n}

0 4 4 7 7 7 8 8 8 9

0 1 2 8 8 8 8 8 8 9

/* Compute when a node becomes a maximum (if it does) for both left-to-right and right-to-left traversals. */

3. For 1 i n in parallel do if LRMax[i-1] LRMax[i] then LRStart[LRMax[i]] = i; if RLMax[i-1] RLMax[i] then RLStart[RLMax[i]] = i;

0 0 0 1 0 0 3 6

1 2 0 0 0 0 0 3

/* Compute when a node is no longer a maximum (if it was) for both left-to-right and right-to-left traversals. */

4. For 1 i n in parallel do if LRMax[i] LRMax[i+1] then LREnd[LRMax[i]] = i; if RLMax[i] RLMax[i+1] then RLEnd[RLMax[i]] = i;

0 0 0 2 0 0 5 8

1 2 0 0 0 0 0 8

/* Compute how many positions a node was maximum for. */5. For 1 i n in parallel do if LRStart[i] 0 then LRSpan[i] = LREnd[i] - LRStart[i] + 1; if RLStart[i] 0 then RLSpan[i] = RLEnd[i] - RLStart[i] + 1;

0 0 0 0 2 0 0 3 3

0 1 1 0 0 0 0 0 6

/* Compute how many nodes a given node is greater than from the left. Similiarly for the right. */

6. Use parallel prefix to construct the array LeftClear and RightClear, where 1 i n LeftClear[i] = LRSpan[0] + … + LRSpan[i-1]; RightClear[i] = RLSpan[0] + … + RLSpan[i-1];

0 0 0 0 2 2 2 5

0 1 2 2 2 2 2 2

/* Removal[i] denotes when the node in Position[i] is removed. */7. For 1 i n in parallel do if RLMax[n-i] > LRMax[i-1] then k = LRMax[i]; Removal[i] = i + RightClear[k]; else k = RLMax[n-i+1]; Removal[i] = (n – i) + 1 + LeftClear[k];

3 4 5 6 7 8 2 1

Theorem on ChainsTheorem on Chains

(Greenlaw & Petreschi)

The Prüfer code of an n-node labeled chain can be computed in O(log n) time using n/log n processors on an

EREW-PRAM.

Parallel Algorithm Prüfer TreeParallel Algorithm Prüfer Tree

Theorem on TreesTheorem on Trees

(Greenlaw, Halldorsson & Petreschi) The Prüfer code of an n-node labeled free tree can be computed in O(log n)

time using n/log n processors on an EREW-PRAM. In the other direction we show Given the Prüfer code of an n-node

labeled chain we can output the corresponding chain in O(log n) time using n/log n processors on an EREW-PRAM.

Prüfer Code SummaryPrüfer Code Summary

Tree to P-code P-code to Tree

sequential

O(n) folklore O(n)

parallel O(log n) time, n processorsEREW-PRAM

O(log n) time, n processorsEREW-PRAM

For a chain O(log n) time,n/log n processors EREW-PRAM

For a chainO(log n) time, n/log n processorsEREW-PRAM

For a treeO(log n) time, n/log n processorsEREW-PRAM

OutlineOutline

Open ProblemsOpen Problems

• Can an optimal parallel algorithm be developed for building a tree given its Prüfer code?

• Can the Element Distinctness Problem be solved on a CREW-PRAM in O(log n) time using n/log n processors?

• Is the edge ranking problem on trees P-complete?• Can all n-node trees be labeled with the

values 1 through n such that neighors receive pairwise relatively prime labels?

ReferencesReferences

See www.cs.armstrong.edu/greenlaw

sequential and parallel algorithms for some problems on trees raymond greenlaw 1 sequential and...

trees raymond greenlaw

node ranking example

parallel prefix problem

example of list ranking

optimal node ranking

nnode tree

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sapienza slide

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