Power Management(Application of Autonomic

Computing Concepts)

Omer Rana

Requirements• Power an important design constraint:

– Electricity costs– Heat dissipation

• Two key options in clusters – enable scaling of:– Operating frequency (square relation)– Supply voltage (cubic relation)

• Balance QoS requirements – e.g.fraction of workload to process locally – with power management

From: Salim Hariri, Mazin Yousif

From: Justin Moore, Ratnesh Sharma, Rocky Shih, Jeff Chase, Chandrakant Patel, Partha Ranganathan (HP Labs)

The case for power management in HPC• Power/energy consumption a critical issue

– Energy = Heat; Heat dissipation is costly– Limited power supply– Non-trivial amount of money

• Consequence– Performance limited by available power– Fewer nodes can operate concurrently

• Opportunity: bottlenecks– Bottleneck component limits performance of other components– Reduce power of some components, not overall performance

• Today, CPU is:– Major power consumer (~100W),– Rarely bottleneck and– Scalable in power/performance (frequency & voltage)

Power/performance“gears”

Is CPU scaling a win?• Two reasons:

1. Frequency and voltage scalingPerformance reduction less than Power reduction

2. Application throughputThroughput reduction less thanPerformance reduction

• Assumptions– CPU large power consumer– CPU driver– Diminishing throughput gains

performance (freq)

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performance (freq)

(1)

(2)

CPU powerP = ½ CVf2

AMD Athlon-64• x86 ISA• 64-bit technology• Hypertransport technology – fast memory bus• Performance

– Slower clock frequency– Shorter pipeline (12 vs. 20)– SPEC2K results

• 2GHz AMD-64 is comparable to 2.8GHz P4• P4 better on average by 10% & 30% (INT & FP)

• Frequency and voltage scaling– 2000 – 800 MHz– 1.5 – 1.1 Volts

From: Vincent W. Freeh (NCSU)

LMBench results• LMBench

– Benchmarking suite– Low-level, micro data

• Test each “gear”

Gear Frequency (Mhz)

Voltage

0 2000 1.51 1800 1.42 1600 1.33 1400 1.24 1200 1.16 800 0.9

From: Vincent W. Freeh (NCSU)

Operating system functions

From: Vincent W. Freeh (NCSU)

Communication

From: Vincent W. Freeh (NCSU)

The problem• Peak power limit, P

– Rack power– Room/utility– Heat dissipation

• Static solution, number of servers is– N = P/Pmax

– Where Pmax maximum power of individual node

• Problem– Peak power > average power (Pmax > Paverage)

– Does not use all power – N * (Pmax - Paverage) unused– Under performs – performance proportional to N– Power consumption is not predictable

From: Vincent W. Freeh (NCSU)

Safe over provisioning in a cluster• Allocate and manage power among M > N nodes

– Pick M > N• Eg, M = P/Paverage

– MPmax > P

– Plimit = P/M

• Goal– Use more power, safely under limit– Reduce power (& peak CPU performance) of individual nodes– Increase overall application performance

time

powe

r Pmax

Paverage

P(t)time

powe

r

PlimitPaverage

P(t)

Pmax

From: Vincent W. Freeh (NCSU)

Safe over provisioning in a cluster

• Benefits– Less “unused” power/energy– More efficient power use

• More performance under same power limitation – Let P be performance

– Then more performance means: MP * > NP– Or P */ P > N/M or P */ P > Plimit/Pmax

time

powe

r Pmax

Paverage

P(t)time

powe

r

PlimitPaverage

P(t)

Pmax

unusedenergy

From: Vincent W. Freeh (NCSU)

When is this a win?

• When P */ P > N/M

or P */ P > Plimit/Pmax

In words: power reduction more than performance reduction

• Two reasons:1. Frequency and voltage scaling2. Application throughput

performance (freq)

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P * / P

< P av

erage

/P max

P * / P

> P av

erage

/P max

performance (freq)

(1)

(2)

From: Vincent W. Freeh (NCSU)

Feedback-directed, adaptive power control

• Uses feedback to control power/energy consumption– Given power goal– Monitor energy consumption– Adjust power/performance of CPU

• Several policies– Average power

– Maximum power

– Energy efficiency: select slowest gear (g) such that

From: Vincent W. Freeh (NCSU)

A more holistic approach: Managing a Data Center

From: Justin Moore, Ratnesh Sharma, Rocky Shih, Jeff Chase, Chandrakant Patel, Partha Ranganathan (HP Labs)

From: Justin Moore, Ratnesh Sharma, Rocky Shih, Jeff Chase, Chandrakant Patel, Partha Ranganathan (HP Labs)

From: Justin Moore, Ratnesh Sharma, Rocky Shih, Jeff Chase, Chandrakant Patel, Partha Ranganathan (HP Labs)

From: Justin Moore, Ratnesh Sharma, Rocky Shih, Jeff Chase, Chandrakant Patel, Partha Ranganathan (HP Labs)

From: Justin Moore, Ratnesh Sharma, Rocky Shih, Jeff Chase, Chandrakant Patel, Partha Ranganathan (HP Labs)

From: Justin Moore, Ratnesh Sharma, Rocky Shih, Jeff Chase, Chandrakant Patel, Partha Ranganathan (HP Labs)

From: Justin Moore, Ratnesh Sharma, Rocky Shih, Jeff Chase, Chandrakant Patel, Partha Ranganathan (HP Labs)

From: Justin Moore, Ratnesh Sharma, Rocky Shih, Jeff Chase, Chandrakant Patel, Partha Ranganathan (HP Labs)