PARALLEL GRAPH PARTITIONING ON A HYPERCUBE

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PARALLEL GRAPH PARTITIONING ON A HYPERCUBE. DISTRIBUTED GENERATION OF PAIRWISE COMBINATIONS. F. Ercal, P. Sadayappan, and J. Ramanujan University of Missouri-Rolla and The Ohio State University. PROBLEM DEFINITION. Given a graph G(V,E), |V|=N |E|=e

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PARALLEL GRAPH PARTITIONING ON A HYPERCUBE

DISTRIBUTED GENERATION OF PAIRWISE COMBINATIONS

F. Ercal, P. Sadayappan, and J. Ramanujan

University of Missouri-Rolla and The Ohio State University

PROBLEM DEFINITION
• Given a graph G(V,E), |V|=N |E|=e
• Obtain a K partitions from G with the following constraints:
• Balanced: Each partition has equal size
• Minimum cut: number of edges across partition is minimized
• arises in: TasK Allocation, VLSI layout, File Placement etc.
• Intractable, no polynomial time algorithm is Known
• Heuristics needed
• Kernighan-Lin Mincut Heuristic (1970)
• Time complexity: O(N2logN)
• Extension by Fiduccia and Mattheyses (1982)
• Used Buckets and moves. Linear time algorithm: O(e)

P1

P2

0

-2

v7

v1

+2

+1

v2

v6

-2

v3

-1

v8

+1

+1

v4

v5

-2

0

v7

v1

v2

-1

v6

-2

v3

-3

v8

+1

+1

v4

v5

MINCUT ALGORITHM

CUT=5

IF V2 MOVES GAIN=2 and TOT_GAIN=2

CUT=3

IF V5 MOVES GAIN=1 and TOT_GAIN=3

MINCUT ALGORITHM (Contd..)

-2

0

v7

v1

v2

-1

v6

v5

-2

v3

-3

v8

v4

-1

CUT=2

IF V1 MOVES GAIN=0 and TOT_GAIN=3

TIME COMPLEXITY

Sequential Time Complexity for Recursive Bisection

N + 2*(N/2) + 4*(N/4) + …….2p*(N/2p) ===> O(N*logK)

Parallel Time Complexity for Recursive Bisection

N + N/2 + N/4 + ……. N/2p ===> O(N)

• COMMENT:
• speedup is very limited
• to increase speedup, bisection algorithm must be parallelized

P1

P2

P3

P6

P4

P5

P7

P8

PAIRWISE MINCUT

PAIRS TO BE CONSIDERED FOR MINCUT

(1,2) (1,3) (1,4) (1,5) (1,6) (1,7) (1,8)

(2,3) (2,4) ………….. (2,8)

…….

(7,8)

TIME COMPLEXITY

Sequential Time Complexity for Pairwise Mincut

Parallel Time Complexity for Recursive Bisection

(100% processor utilization)

• CONCLUSIONS
• Sequential Recursive Bisection (RB) has much lower time complexity than Pairwise Mincut (PM)
• but superior parallelizability of PM renders its parallel time complexity comparable to that of parallel RB

1) RECURSIVE BISECTION

• Perform repeated bisection, each time doubling the number of partitions, until K partitions are obtained

Time Complexity

N+ 2*(N/2) + 4*(N/4)+….+2P*(N/2P) ==> O(N*logK)

2) PAIRWISE MINCUT

• Initially obtain K partitions. Try to reduce the cut-size between each pair of partitions. K(K-1)/2 pairs (each of size 2N/K) must be considered

Time Complexity

3) Any combination of

RECURSIVE BISECTION+PAIRWISE MINCUT

Problem

• Given 2P disjoint items, P*(2P-1) distinct pairs can be formed.
• How would you efficiently generate these pairs on the processors of a hypercube ?
• Similar to the problem of distributed scheduling of a round-robin tournament between 2C players using C courts, where the paths between courts form a hypercube topology
• maximum utilization of courts (processor utilization)
• +
• minimum walking between courts (min. comm. overhead)

C2

C1

C2

C1

B00

B01

B10

B11

P00

P01

A00

A01

A10

A11

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C1

C2

C1

C2

C1

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C1

C2

A00

A01

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A11

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B01

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C1

C2

d=0

d=1

d=2

P00

P01

P10

P11

A00

A01

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A11

B00

B01

B10

B11

P00

Distributed PC Algorithm on a 2d Hypercube (4 Processors)

A1

A2

A3

:

:

AK/2

AK/2+1

:

:

AK

B1

B2

B3

:

:

BK/2

BK/2+1

:

:

BK

1

CYCLIC-TOUR

RING-FRAGMENTATION

2

A1

A2

:

AK/4

AK/4+1

:

AK/2

AK/2

AK/2+1

:

A3K/4

A3K/4+1

:

AK

B1

B2

:

BK/4

BK/4+1

:

BK/2

BK/2

BK/2+1

:

B3K/4

B3K/4+1

:

BK

CYCLIC-TOUR

CYCLIC-TOUR

RING-FRAGMENTATION

0110

0111

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1111

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1100

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(a) d=0 1 ring of size 16

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0100

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0001

(b) d=1 2 rings of size 8

Ring Communication in different phases of Distributed PC algorithm (Contd..)

1110

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0110

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(c) d=2 4 rings of size 4

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(d) d=3 8 rings of size 2

Ring Communication in different phases of Distributed PC algorithm (Contd..)

1110

1111

0110

0111

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0100

1110

0110

1010

1011

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(e) d=4 16 rings of size 1