EE 382C - S11 - Lecture 13 1

EE382C

Lecture 13

Network Performance Analysis

5/12/11

EE 382C - S11 - Lecture 13 2

Announcements

• I will be out all next week (IPDPS in Alaska)

– No office hours

– George doing both lectures

• Project sign-up (May 24th and 26th)

– 15 min. presentation + 3 min. for questions

• Final report due (May 31st -- 5pm)

EE 382C - S11 - Lecture 13 3

So far

• Topology

• Routing

• Flow Control / Deadlock

• Microarchitecture – datapath/control

EE 382C - S11 - Lecture 13 4

Question of the day

What is the best way to evaluate an interconnection network?

a) analysis

b) simulation

c) experiment

EE 382C - S11 - Lecture 13 5

Question of the day II

What metrics should you be worried about when evaluating an

interconnection network?

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Network Performance

• Latency

• Throughput

• Steady state

• Open loop

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T

Applied Load-Latency

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Offered vs. Accepted

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Network StabilityThroughput at maximum load vs peak throughput

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BNF chart

(Why is this wrong?)

How can you design a stable network?

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Throughput vs Offered Traffic for Stable Network8x8 Mesh, DOR, Bit Complement

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EE 382C - S11 - Lecture 13 13

Simple network

0 1

1

0.1

01

10

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Simple network

0 1

1

0.1

01

10

At what load does the simple network saturate?

Measure the minimum throughput over

source-destination pairs, not the average

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0.10 1

1 0

0.9

0 0

1 0

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3 ways to measure performance

• Analysis

• Simulation

• Experiment

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Analysis

Example – switch speedup analysis

• Probability that an input requests a particular output

• Probability that an input doesn’t request a particular output

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Pi 1

k

PNi k 1

k

• Probability that no input requests a particular output

• Probability that some input requests a particular output

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PNa k 1

k

k

Pa 1k 1

k

k

• Pa is the throughput of a switch with unit speedup.

• If we add input speedup this number goes up – but is still

bounded by 1

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Pa 1k 1

k

si k

• Adding output speedup increases overall throughput by so

and makes input speedup effectively si/so

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Pa so 1k 1

k

si

so

k

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Analysis

Example – queuing theory

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Network to Analyze

0

1

2

3

Switch

00

Switch

01

Switch

10

Switch

11

0

1

2

3

EE 382C - S11 - Lecture 13 24

Model

T

0

T1

T2 T

T0

T1

T

0

T1

T0

T1

T2 T

T2 T

T2 T

Switch 00

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Result

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Folded Clos network example

0

5

10

15

20

25

30

35

40

0 0.2 0.4 0.6 0.8 1

Offered load

La

ten

cy

(c

yc

les

)

oblivious - folded Clos oblivious - Clos M/D/1 model

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Simulation Workload

• Application-Driven

• Trace-Driven

• Synthetic workload

– Traffic pattern

– Injection Process

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Injection process

time

Inje

ction

pro

cess

T = 1/r

time

Inje

ction

pro

cess

E[T] = 1/r

Simulation Setup

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Simulation Procedure

• Warm up the simulator with “no measurement” packets

– Make sure you are in steady-state before taking a steady state

measurement

• Once in steady state, inject “measurement packets” long

enough to get a statistically meaningful measurement

– Apply the usual tests and compute error bounds

• Drain the simulator while injecting “no measurement”

packets until all “measurement packets” arrive at their

destinations.

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Include an infinite Source Queue when

measuring Latency Do not do the following:

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EE 382C - S11 - Lecture 13 32

Simulation results – Virtual channels

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Packet sizes

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Age based priority

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Question of the day

What is the best way to evaluate an interconnection network?

a) analysis

b) simulation

c) experiment

EE 382C - S11 - Lecture 13 36

Question of the day II

What metrics should you be worried about when evaluating an

interconnection network?

Errors to Avoid

• Don’t forget the source queue

• Measure minimum traffic across pairs

• Don’t combine latency with accepted traffic

• Measure all “measurement packets” generated during test

interval

– May require a long draining period

• Use realistic traffic – not just UR

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EE 382C - S11 - Lecture 13 38

Summary of performance analysis

• Analysis, simulation, experiment

– Maximize insight / effort

• Measurements – steady state, open loop, on specified

– Warm up, source queue, min throughput

• Queueing theory

– M/M/1 N = r1r

• Simulation

– Workloads

– Errors – systematic and sampling

– Confidence intervals and ensemble averages