EE384y: Packet Switch Architectures Matchings, implementation and heuristics

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EE384y: Packet Switch Architectures Matchings, implementation and heuristics

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EE384y: Packet Switch Architectures Matchings, implementation and heuristics

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EE384y: Packet Switch Architectures

Matchings, implementation and heuristics

Nick McKeown

Professor of Electrical Engineering

and Computer Science, Stanford University

nickm@stanford.edu

www.stanford.edu/~nickm

- Finding a maximum match.
- Maximum network flow problems
- Definitions and example
- Augmenting paths

- Maximum size/weight matchings as examples of maximum network flows
- Maximum size matching
- Complexity of maximum size matchings and maximum weight matchings

- Maximum network flow problems
- What algorithms are used in practice?
- Maximal Matches
- Wavefront Arbiter (WFA)
- Parallel Iterative Matching (PIM)
- iSLIP

10

10

10

1

10

1

10

1

a

c

Source

s

Sink

t

b

d

- Let G = [V,E] be a directed graph with capacity cap(v,w) on edge [v,w].
- A flow is an (integer) function, f, that is chosen for each edge so that
- We wish to maximize the flow allocation.

10

10

10

1

10

1

10

1

a

c

10, 10

Source

s

Sink

t

10, 10

1

10, 10

10

1

10

b

d

1

Flow is of size 10

a

c

Source

s

Sink

t

b

d

Step 1:

Not

obvious

Maximum flow:

a

c

10, 9

Source

s

Sink

t

10, 10

1,1

10, 10

1,1

10, 2

b

d

10, 2

1, 1

Flow is of size 10+2 = 12

Step 2:

a

c

10, 10

Source

s

Sink

t

10, 10

1

10, 10

1

10, 1

b

d

10, 1

1, 1

Flow is of size 10+1 = 11

- Set f(v,w) = -f(w,v) on all edges.
- Define a Residual Graph, R, in which res(v,w) = cap(v,w) – f(v,w)
- Find paths from s to t for which there is positive residue.
- Increase the flow along the paths to augment them by the minimum residue along the path.
- Keep augmenting paths until there are no more to augment.

a

c

10, 10

10, 10

1

10, 10

s

t

10

1

10

b

d

1

Flow is of size 10

Residual Graph, R

res(v,w) = cap(v,w) – f(v,w)

a

c

10

10

10

1

s

t

10

1

10

b

d

1

Augmenting path

Step 2:

a

c

10, 10

s

t

10, 10

1

10, 10

1

10, 1

b

d

10, 1

1, 1

Flow is of size 10+1 = 11

Residual Graph

a

c

10

s

t

10

10

1

1

1

1

b

d

9

1

9

Step 3:

a

c

10, 9

s

t

10, 10

1, 1

10, 10

1, 1

10, 2

b

d

10, 2

1, 1

Flow is of size 10+2 = 12

1

Residual Graph

a

c

9

s

t

10

10

1

2

1

2

b

d

8

1

8

- In general, it is possible to find a solution by considering at most |V|.|E| paths, by picking shortest augmenting path first.
- There are many variations, such as picking most augmenting path first.

- Finding a maximum match.
- Maximum network flow problems
- Definitions and example
- Augmenting paths

- Maximum size/weight matchings as examples of maximum network flows
- Maximum size matching
- Complexity of maximum size matchings and maximum weight matchings

- Maximum network flow problems
- What algorithms are used in practice?
- Maximal Matches
- Wavefront Arbiter (WFA)
- Parallel Iterative Matching (PIM)
- iSLIP

A

1

2

B

3

C

4

D

5

E

6

F

- How do we find the maximum size (weight) match?

A

1

2

B

Finding a maximum size bipartite matching is equivalent to solving a network flow problem with capacities and flows of size 1.

Sink

t

Source

s

3

C

4

D

5

E

6

F

Residual Graph for first three paths:

A

1

2

B

t

s

3

C

4

D

5

E

6

F

Residual Graph for next two paths:

A

1

2

B

t

s

3

C

4

D

5

E

6

F

Residual Graph for augmenting path:

A

1

2

B

t

s

3

C

4

D

5

E

6

F

Residual Graph for last augmenting path:

A

1

2

B

t

s

3

C

4

D

5

E

6

F

Note that the path augments the match: no input and output

is removed from the match during the augmenting step.

Maximum flow graph:

A

1

2

B

t

s

3

C

4

D

5

E

6

F

Maximum Size Matching:

A

1

2

B

3

C

4

D

5

E

6

F

- Maximum Size Matchings:
- Algorithm by Dinic O(N5/2)

- Maximum Weight Matchings
- Algorithm by Kuhn O(N3)

- In general:
- Hard to implement in hardware
- Slooooow.

- Finding a maximum match.
- Maximum network flow problems
- Definitions and example
- Augmenting paths

- Maximum size/weight matchings as examples of maximum network flows
- Maximum size matching
- Complexity of maximum size matchings and maximum weight matchings

- Maximum network flow problems
- What algorithms are used in practice?
- Maximal Matches
- Wavefront Arbiter (WFA)
- Parallel Iterative Matching (PIM)
- iSLIP

- A maximal matching is one in which each edge is added one at a time, and is not later removed from the matching.
- i.e. no augmenting paths allowed (they remove edges added earlier).
- No input and output are left unnecessarily idle.

A

1

A

1

A

1

2

B

2

B

2

B

3

C

3

C

3

C

4

D

4

4

D

D

5

E

5

5

E

E

6

F

6

6

F

F

Maximal

Size Matching

Maximum

Size Matching

- In general, maximal matching is simpler to implement, and has a faster running time.
- A maximal size matching is at least half the size of a maximum size matching.
- A maximal weight matching is defined in the obvious way.
- A maximal weight matching is at least half the weight of a maximum weight matching.

- Finding a maximum match.
- Maximum network flow problems
- Definitions and example
- Augmenting paths

- Maximum size/weight matchings as examples of maximum network flows
- Maximum size matching
- Complexity of maximum size matchings and maximum weight matchings

- Maximum network flow problems
- What algorithms are used in practice?
- Maximal Matches
- Wavefront Arbiter (WFA)
- Parallel Iterative Matching (PIM)
- iSLIP

Requests

Match

1

1

1

1

2

2

2

2

3

3

3

3

4

4

4

4

Requests

Match

2,4

1,4

4,4

3,4

4,3

4,2

4,1

3,3

3,2

3,1

2,2

2,1

1,3

1,2

1,1

2,3

Simple combinational logic blocks

N steps instead of

2N-1

Match

Requests

- Feed-forward (i.e. non-iterative) design lends itself to pipelining.
- Always finds maximal match.
- Usually requires mechanism to prevent Q11 from getting preferential service.
- In principle, can be distributed over multiple chips.

- Finding a maximum match.
- Maximum network flow problems
- Definitions and example
- Augmenting paths

- Maximum size/weight matchings as examples of maximum network flows
- Maximum size matching
- Complexity of maximum size matchings and maximum weight matchings

- Maximum network flow problems
- What algorithms are used in practice?
- Maximal Matches
- Wavefront Arbiter (WFA)
- Parallel Iterative Matching (PIM)
- iSLIP

Iteration:

1

1

1

1

2

2

2

2

#1

1

1

3

3

3

3

2

2

4

4

4

4

f2: Grant

f3: Accept/Match

3

3

1

1

1

1

1

1

4

4

2

2

2

2

2

2

#2

3

3

3

3

3

3

4

4

4

4

4

4

uar selection

uar selection

f1: Requests

- Guaranteed to find a maximal match in at most N iterations.
- In each phase, each input and output arbiter can make decisions independently.
- In general, will converge to a maximal match in < N iterations.
- How many iterations should we run?

Number of iterations to converge:

PIM with a single iteration

PIM with 4 iterations

- Finding a maximum match.
- Maximum network flow problems
- Definitions and example
- Augmenting paths

- Maximum size/weight matchings as examples of maximum network flows
- Maximum size matching
- Complexity of maximum size matchings and maximum weight matchings

- Maximum network flow problems
- What algorithms are used in practice?
- Maximal Matches
- Wavefront Arbiter (WFA)
- Parallel Iterative Matching (PIM)
- iSLIP

1

4

2

1

1

1

1

3

2

2

2

2

#1

1

1

3

3

3

3

2

2

4

4

4

4

F2: Grant

F3: Accept/Match

3

3

1

1

1

1

1

1

4

4

2

2

2

2

2

2

#2

3

3

3

3

3

3

4

4

4

4

4

4

F1: Requests

- Grant phase: Each output selects the requesting input at the pointer, or the next input in round-robin order. It only updates its pointer if the grant is accepted.
- Accept phase: Each input selects the granting output at the pointer, or the next output in round-robin order.
- Consequence: Under high load, grant pointers tend to move to unique values.

- Random under low load
- TDM under high load
- Lowest priority to MRU
- 1 iteration: fair to outputs
- Converges in at most N iterations. (On average, simulations suggest < log2N)
- Implementation: N priority encoders
- 100% throughput for uniform i.i.d. traffic.
- But…some pathological patterns can lead to low throughput.

Programmable

Priority Encoder

1

State

1

log2N

Decision

N

Grant

Accept

2

2

N

Grant

Accept

log2N

N

N

Grant

Accept

log2N

N

- Maximal matching algorithms are widely used in industry (PIM, iSLIP, WFA and others).
- PIM and iSLIP are rarely run to completion (i.e. they are sub-maximal).
- We will see shortly that a maximal match with a speedup of 2 is stable for non-uniform traffic.