03-powerOf3DRendering-BPS2011-koduri

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Power of Realtime 3D-RendeRaja Koduri


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We ate our GPU cake -

And had more too! 16+ years of (sugar) high!

In every GPU generation More performance and performance-per-watt

More programmability, precision and features

While maintaining compatibility with 8+ generati

APIs

vuoi la botte piena e la moglie ubriaca


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Chip Power = Static Power + Dynamic Power

System Power = CPU + GPU + Other

Static power is leakage of inactive transistors

Static Power = N*V*e-Vt

Dynamic power is from active switching transistors

Dynamic Power = A*N*C*F*V2

N - Number of transistors

V - Voltage

C - Capacitance per transistor

F - Frequency

Vt - Thresh-hold Voltage

A - Activity Factor

Energy = Power(Watts) * Time


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Desktops

0-10 Watts TDP 10-50 Watts TDP 50-300 Watts T

Mobile Devices

(Phones, tablets etc)

Mobile Computers

(Computers with abattery)

Desktops, ServeConsoles etc

(Always plugged

TDP - Thermal design power

Maximum amount of power that the thermal system csustain


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Sub Moores law scaling of performance-per-w N wants to go up, but Sub-Moore scaling on C and V.

GPUs scale better than CPUs

Beware of the Fine Print though


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Towards lower power computers

Battery lifeThermal limits

Acoustics

$-per-KiloWattHourGreen


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0 W

10 W

50 W

300 W

Mobile Devices

Desktops

Mobile Computers

HW vendors compelled to succeed in mobile


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Chip Power = N*V*e-Vt + A*N*C*F*V2

CPUs

Prioritize Frequency - higher V and Vt

Spend N for caches, cores, flexibility and compute qu

GPUs

Lower Frequency and Voltage

Spend N for shaders, textures, pixels etc

FixedFunction

Lowest N, F and V for a given task


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If your workload parallelizes well on a GPUUse the GPU

Optimize for System Energy

Its only a win if

GPU_Workload_power*GPU_Executiontime +CPU_GPU_feeding_power*CPU_GPU_feeding_t is less thanCPU_Workload_power*CPU_Executiontime


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Power off unused areas(Power gating)When not in use Static Power ~= 0

Fine print Latency with power toggle (few nano-seconds to a few hundred microsecond

Too aggressive switching may cause performance problems, too conservativswitching may lead to wasted power



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Primary OS+HW strategy is to control F & V

Based on history of A

Applications have control of Activity(A)

Lower A, leads to lower F and V and lower power.

Fine Print

Switching F&V states can range between a few milliseconds to few secon

Dynamic Power = A*N*C*F*V2


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0

0.3

0.5

0.8

1.0

Activity/Performance/Powersampleillustration

Activity Frequency Power

Time

Ac

tivity/Frequency/Power

State Switch latency


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Applications

GPU API/Runtime

GPU UserMode Drivers

GPU Kernel Mode DriverDrivers

GPU Power ManagementDriver

GPUPower u-Controller


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Control frame rate to minimum desired

Pumping out more frames than user can see is

wasteful anyways

Tip 1


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0 10 20 30 40 50 60 70 80 90

400

0

50

100

150

200

250

300

350

50 Watt System

Frames-per-sec

Power in Watts

Power Limit @50W

Simple rendering (like menus)

Complex re(like game p


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0 10 20 30 40 50 60 70 80 90

400

0

50

100

150

200

250

300

350

25 Watt Syste

Frames-per-sec

Power in Watts

PowerLimit@25W

Simple rendering (like menus)

Complex ren(like game p

Thermal limit

60 fps app


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Optimize the frame rendering time to a minimum

Dont stop optimizing when you hit your minimum

frame rate targets!

Tip 2


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100

0

10

20

30

40

50

60

70

80

90

Time in Milliseconds

Activity

Case1 Energy 4*Pmax+12*Pstatic

Pstatic is near zero in power optimized GPUs,So

Case1 Energy 4*Pmax


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100

0

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Activity

Case1 Energy 4*Pmax

Case2 Energy 16*Pmax


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Dont scatter work in a frame (coalesce)

Insufficient idle intervals for power-state reduction

Tip 3


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100

0

10

20

30

40

50

60

70

80

90


Activity

May not be enough idle time for switching to lower powe


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Avoid spin-loops

Tip 4

Eg:- CPU waiting on GPU

This looks like real work to CPU


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A complex subject

Dynamic scheduling systems based on power thermal feedback

Hardware v/s Software schedulers

Scheduling CPU and GPU

Many more topics


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Premature declaration of death of fixed-fu

in GPU hardware

What new candidates can we move to

FixedFunction?

What interface principles should

FixedFunction hardware adopt to be

mainstream programmer friendly?


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Can we build Predictive models toaugment current reactive models?

Should there be APIs for apps to influenpower states and monitor feedback?


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2560x1600 Screen @60Hz ~ 250 MPixels/Sec

A 25W GPU today

5800 MPixels/Sec17400 MTexels/Sec696000 MFLOPS


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Reduction Choices Reduce N, but that reduces capabilities

Reduce V, but limits performance and not in yourcontrol(process limits)

Reduce Vt, Slower transistor, lower performance


Dominant battery life factor for common usage scenar


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10

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60

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90


Activity

Case1 Energy 4*Pmax

Case3 Energy 8*0.7*P?

Date post:	03-Apr-2018
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