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 scaling Performance reduction less than Power reduction 2.Application throughput Throughput reduction less than Performance reduction Assumptions –CPU large power consumer –CPU driver –Diminishing throughput gains performance (freq) power application throughput performance (freq) (1) (2) CPU power P = ½ CVf 2

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 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/P max –Where P max maximum power of individual node Problem –Peak power > average power (P max > P average ) –Does not use all power – N * (P max - P average ) 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/P average –MP max > P –P limit = P/M Goal –Use more power, safely under limit –Reduce power (& peak CPU performance) of individual nodes –Increase overall application performance time power P max P average P(t) time power P limit P average P(t) P max 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: M P * > N P –Or P * / P > N/M or P * / P > P limit /P max time power P max P average P(t) time power P limit P average P(t) P max unused energy From: Vincent W. Freeh (NCSU)

When is this a win? When P * / P > N/M or P * / P > P limit /P max In words: power reduction more than performance reduction Two reasons: 1.Frequency and voltage scaling 2.Application throughput performance (freq) power application throughput P * / P < P average /P max P * / P > P average /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)