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3 Distributed Counting Some processors request a counter value count

TRANSCRIPT

Concurrent Counting is harder than Queuing

Costas Busch

Rensselaer Polytechnic Intitute

Srikanta Tirthapura

Iowa State University

Arbitrary graph

Distributed Counting

Some processors request a counter value

count

count

count

count

Distributed Counting

Final state

Distributed Queuing

Some processors perform enqueue operations

Enq(A)

Enq(B)

Enq(D)

Enq(C)

A

B

C

D

Distributed Queuing

A

B

C

D

head

Tail

A

B

C

D

Previous=nil

Previous=B

Previous=D

Previous=A

Counting: - parallel scientific applications - load balancing (counting networks)

Queuing:- distributed directories for mobile objects- distributed mutual exclusion

Applications

Multicast with the condition:all messages received at all nodes in the same order

Either Queuing or Counting will do

Which is more efficient?

Ordered Multicast

Queuing vs Counting?

Total orders

Queuing = finding predecessor

Needs local knowledge

Counting = finding rank

Needs global knowledge

Problem

Is there a formal sense in which Counting is harder problem than Queuing?

Reductions dont seem to help

Our Result

Concurrent Counting is harder than Concurrent Queuing

on a variety of graphs including:

many common interconnection topologies

complete graph,

mesh

hypercube

perfect binary trees

Model

Synchronous system G=(V,E) - edges of unit delay

Congestion: Each node can process only one message in a single time step

Concurrent one-shot scenario:

a set R subset V of nodes issue queuing (or counting) operations at time zero

No more operations added later

Cost Model

: delay till v gets back queuing result

Cost of algorithm A on request set R is

Queuing Complexity =

Define Counting Complexity Similarly

Lower Bounds on Counting

Counting Cost =

For arbitrary graphs:

For graphs with diameter :

Counting Cost =

Theorem:

Proof:

Consider some arbitrary algorithm

for counting

For graphs with diameter :

Counting Cost =

Take shortest path of length

Graph

Graph

make these nodes to count

1

2

3

4

5

6

7

8

k

Node of count decides after

at least time steps

Needs to be aware of other processors

k-1

Counting Cost:

End of Proof

Theorem:

Counting Cost =

For arbitrary graphs:

Proof:

Consider some arbitrary algorithm

for counting

Prove it for a complete graph with nodes:

any algorithm on any graph with nodes

can be simulated on the complete graph

The initial state affects the outcome

Red: count

Blue: dont count

Initial State

Final state

Red: count

1

2

3

4

5

Blue: dont count

Initial state

Red: count

Blue: dont count

Final state

Red: count

1

2

3

4

5

6

7

8

Blue: dont count

Let be the set of nodes whose

input may affect the decision of

Suppose that there is an initial state

for which decides

Then:

These two initial states give same result for

If , then would decide

less than

Thus,

Suppose that decides at time

We show:

times

Suppose that decides at time

times

Cost of node :

If nodes wish to count:

Counting Cost =

Nodes that

affect

up to time

Nodes that

affects

up to time

After , the sets grow

Eligible to

send message

at time

There is an initial state such that

that sends a message to

Eligible to

send message

at time

Suppose that

Then, there is an initial state such that

both send message to

Eligible to

send message

at time

However, can receive one message at a time

Eligible to

send message

at time

Therefore:

Number of nodes like :

Therefore:

We can also show:

Thus:

Which give:

times

End of Proof

Upper Bound on Queuing

Queuing Cost =

For graphs with spanning trees

of constant degree:

Queuing Cost =

For graphs whose spanning trees

are lists or perfect binary trees:

An arbitrary graph

Spanning tree

Spanning tree

A

A

Distributed Queue

Head

Tail

Tail

Previous = Nil

A

B

enq(B)

Tail

A

Head

Tail

Previous = ?

Previous = Nil

A

B

enq(B)

Tail

A

Head

Tail

Previous = ?

Previous = Nil

A

B

enq(B)

Tail

A

Head

Tail

Previous = ?

Previous = Nil

A

B

enq(B)

Tail

A

Head

Tail

Previous = ?

Previous = Nil

A

B

enq(B)

Tail

A

Head

Tail

Previous = ?

Previous = Nil

A

A

B

Tail

Previous = A

B

A informs B

Head

Tail

Previous = Nil

A

Concurrent Enqueue Requests

Tail

B

C

A

Head

Tail

enq(B)

enq(C)

Previous = ?

Previous = ?

Previous = Nil

A

Tail

B

C

A

Head

Tail

enq(B)

enq(C)

Previous = ?

Previous = ?

Previous = Nil

A

Tail

B

C

A

Head

Tail

enq(B)

enq(C)

Previous = ?

Previous = ?

Previous = Nil

A

Tail

B

C

enq(B)

A

C

Head

Tail

Previous = A

Previous = ?

Previous = Nil

A

Tail

B

C

enq(B)

A

C

Head

Tail

Previous = A

Previous = ?

Previous = Nil

A

Tail

B

C

A

C

Head

Tail

C informs B

Previous = C

Previous = A

B

Previous = Nil

A

Tail

B

C

A

C

Head

Tail

Previous = C

Previous = A

B

Previous = Nil

A

Tail

B

C

A

C

Head

Tail

Previous = C

Previous = A

B

Previous = Nil

Paths of enqueue requests

Nearest-Neighbor TSP tour

on Spanning tree

A

C

B

D

Origin

(first element in queue)

E

F

A

C

B

Visit closest unused node in Tree

E

D

F

A

C

B

Visit closest unused node in Tree

E

D

F

A

C

B

E

D

F

Nearest-Neighbor TSP tour

Queuing Cost

x Nearest-Neighbor

TSP length

[Herlihy, Tirthapura, Wattenhofer PODC01]

For spanning tree of constant degree:

Nearest-Neighbor

TSP length

Optimal

TSP length

[Rosenkratz, Stearns, Lewis SICOMP1977]

If a weighted graph satisfies

triangular inequality:

x

A

C

D

B

E

F

A

C

D

B

F

2

4

4

1

3

1

2

3

1

weighted graph of distances

E

1

2

3

4

2

1

Satisfies triangular inequality

A

C

D

2

1

1

A

C

D

B

F

2

4

4

1

3

1

2

3

1

E

1

2

3

4

2

1

A

C

E

B

F

D

Nearest Neighbor TSP tour

A

C

D

B

F

2

4

4

1

3

1

2

3

1

E

1

2

3

4

2

1

Length=8

A

C

E

B

F

D

A

C

D

B

F

2

4

4

1

3

1

2

3

1

E

1

2

3

4

2

1

Optimal TSP tour

Length=6

It can be shown that:

Optimal TSP length

A

C

E

B

F

D