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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
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
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)
mincut algorithm

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
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
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
pairwise mincut

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 complexity1
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
slide9

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

distributed generation of pairwise combinations on a hypercube
DISTRIBUTED GENERATION OF PAIRWISE COMBINATIONS ON A HYPERCUBE

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)
slide11

C2

C1

C2

C1

B00

B01

B10

B11

P00

P01

A00

A01

A10

A11

P10

P11

C1

C2

C1

C2

C1

C2

C1

C2

A00

A01

P01

A10

A11

P10

B00

B01

P11

B10

B11

C1

C2

d=0

d=1

d=2

P00

P01

P10

P11

A00

A01

A10

A11

B00

B01

B10

B11

P00

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

slide12

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

ring communication in different phases of distributed pc algorithm
Ring Communication in different phases of Distributed PC algorithm

0110

0111

1110

1111

0100

1100

0110

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1010

1011

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1001

(a) d=0 1 ring of size 16

1110

1111

0110

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1110

0110

1010

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0011

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0000

1001

0001

(b) d=1 2 rings of size 8

ring communication in different phases of distributed pc algorithm contd
Ring Communication in different phases of Distributed PC algorithm (Contd..)

1110

1111

0110

0111

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0100

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(c) d=2 4 rings of size 4

1110

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(d) d=3 8 rings of size 2

ring communication in different phases of distributed pc algorithm contd1
Ring Communication in different phases of Distributed PC algorithm (Contd..)

1110

1111

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(e) d=4 16 rings of size 1

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