Modeling CPU Energy Consumption for Energy Efficient Scheduling

Abhishek Jaiantilal, Yifei Jiang, Shivakant Mishra

University of Colorado - Boulder

GCM '10 Proceedings of the 1st Workshop on Green Computing © 2010 ACM

Outline

• Introduction

• Energy Model Overview

• Power Consumed and CPU Cycles

• Experimental Results

• Conclusions

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Introduction (1/2)

• The processor is the component that consumes the

most power.

3

Introduction (2/2)

• Dynamic Voltage and Frequency Scaling (DVFS) is used

in CPU, referring as P-states.

• Per Core Power Gating (PCPG), or Dynamic Core Gating

(DCG) is a hardware feature allowing the cores in a multi-

core CPU to shut themselves off. It is also called C-states.

– C0 - Active state

– C1 - Inactive state with the core not running on these idle cycles

– C3 - Inactive state with the cache saved

– C6 - All the PLL turned off

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Energy Model Overview (1/3)

• Black Box approach

– PCPG is hardware controlled, so we use Black

Box approach.

• Obtained the statistics of /proc/stat file

– A scheduling policy to limit these loops on few

cores might not be the best compared with

running them on all the cores.

• Still a low power profile.

• Lesser execution time.

• So we need to know the power consumption of a task

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Energy Model Overview (2/3)

6

• Even though the processes are running at 100% load, the

power consumed is different for different tasks.

• Because some of these tasks are float-cycle intensive and

others are integer or memory cycle intensive.

Energy Model Overview (3/3)

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• Modified Black Box approach

– If we know how much power a task is consuming, then

we can fit a schedule that allowing for a shorter

execution time and a lower energy consumption.

– We need the “training data” to choose the best task

schedule depending on the tradeoff between the power

consumption and the execution time.

• Disadvantages

– Need training data from all the possible tasks first

– Computers should have the same configuration

Power Consumed and CPU Cycles (1/7)

• System power consumption

𝑃(𝑆𝑦𝑠𝑡𝑒𝑚)∝ 𝑓(𝑃 𝐶𝑃𝑈 + 𝑃 𝑀𝑒𝑚𝑜𝑟𝑦 + 𝑃 𝐹𝑎𝑛𝑠+ 𝑃 𝐻𝐷𝐷 + 𝑃 𝑁𝑜𝑟𝑡ℎ𝑏𝑟𝑖𝑑𝑔𝑒+ 𝑃 𝑆𝑜𝑢𝑡ℎ𝑏𝑟𝑖𝑑𝑔𝑒 + 𝑃 𝐺𝑟𝑎𝑝ℎ𝑖𝑐𝑠+ 𝑃(𝑂𝑡ℎ𝑒𝑟 𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡𝑠))

– f() = Efficiency of the Power supply

8

Power Consumed and CPU Cycles (2/7)

• Simplified system power consumption

𝑃(𝑆𝑦𝑠𝑡𝑒𝑚) ∝ 𝑃 𝐶𝑃𝑈 + 𝑃 𝑀𝑒𝑚𝑜𝑟𝑦 + 𝑃 𝐵𝑖𝑎𝑠

– Bias = Power of Fans, Motherboard, North-bridge,

South-bridge, Graphics, HDD, and Other Components.

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Power Consumed and CPU Cycles (3/7)

• We proposed if we know the CPU cycle profile for a

task, we can build a simple linear model to account the

CPU load and energy consumed.

𝑃 𝑆𝑦𝑠𝑡𝑒𝑚∝ 𝐶𝑦𝑐𝑙𝑒𝑠 𝐹𝑃𝑈 + 𝐶𝑦𝑐𝑙𝑒𝑠 𝐼𝑁𝑇+ 𝐶𝑦𝑐𝑙𝑒𝑠 𝑀𝑒𝑚𝑜𝑟𝑦 + 𝑃(𝐵𝑖𝑎𝑠)

𝑃(𝑇𝑎𝑠𝑘𝑖)∝ 𝐶𝑦𝑐𝑙𝑒𝑠 𝐹𝑃𝑈 + 𝐶𝑦𝑐𝑙𝑒𝑠 𝐼𝑈 + 𝐶𝑦𝑐𝑙𝑒𝑠 𝐶𝑎𝑐ℎ𝑒

𝑃 𝑆𝑦𝑠𝑡𝑒𝑚 ∝ 𝑃𝑜𝑤𝑒𝑟 𝑇𝑎𝑠𝑘𝑖

𝑁

𝑖=1

+ 𝐵𝑖𝑎𝑠

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Power Consumed and CPU Cycles (4/7)

• We need to know the counts and the types of CPU

cycles executed by a task.

– Dtrace for Solaris

– Oprofile

– Intel Vtune for Linux

• We used Vtune in an offline manner and sampled

the application and store the cycle time over some

period. (30 minutes~1 hour)

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Power Consumed and CPU Cycles (5/7)

• Linear Regression Model

𝑃𝑜𝑤𝑒𝑟 𝑇𝑎𝑠𝑘𝑖 = 𝐹 ∗ 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓𝐹𝑃 𝑐𝑦𝑐𝑙𝑒𝑠 +𝐼 ∗ 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐼𝑛𝑡 𝐶𝑦𝑐𝑙𝑒𝑠 +𝑀 ∗ 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑀𝑒𝑚𝑜𝑟𝑦 𝐶𝑦𝑐𝑙𝑒𝑠

• F, I, and M are multiplier for watt cost of running a

single FP, INT, or Memory cycle. But there is no

direct way to find them.

12

Power Consumed and CPU Cycles (6/7) • We use the statistical approach of minimizing the square

error to find these unknown variables.

min𝐹,𝐼,𝑀

𝑀𝑒𝑎𝑠𝑢𝑟𝑒𝑑 𝑤𝑎𝑡𝑡𝑎𝑔𝑒 𝑌 − 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 𝑤𝑎𝑡𝑡𝑎𝑔𝑒 𝑌 2

𝑌 = 𝐹 ∗ 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓𝐹𝑃 𝑐𝑦𝑐𝑙𝑒𝑠 +𝐼 ∗ (𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐼𝑛𝑡 𝐶𝑦𝑐𝑙𝑒𝑠) +𝑀 ∗ 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑀𝑒𝑚𝑜𝑟𝑦 𝐶𝑦𝑐𝑙𝑒𝑠 + 𝐵𝑖𝑎𝑠 = Xβ 𝐹, 𝐼,𝑀 > 0, β = 𝐹 𝐼 𝑀

• Once we know X, Y, then F, I, and M (stored in the β

vector) can be obtained as:

β = 𝑋𝑇𝑋 + 𝜆𝐼 −1𝑋𝑇𝑌

13

Power Consumed and CPU Cycles (7/7)

• We also used another statistical algorithm -

Random Forests in our experiments.

– Random Forests is a popular machine learning/statistical

approach that uses decision trees.

– It is a non-linear algorithm compared to the linear

regression formulation.

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Experimental Results (1/6)

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• Regression Model Training

– We obtained training data from the following

benchmarks first:

• memcpy

• While-float

• mprime

– Then we obtained separated test data for:

• SPECjvm

• While-Int

• While-Branch

Experimental Results (2/6)

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• Results of Regression Model

Experimental Results (3/6)

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• Energy Efficient Scheduler

– We proposed that we do not wake up a core from idle state until its needed. The cores that were not allocated any tasks were shut off.

– A core cannot execute more than a specific number of processor cycles.

– We used the average number of cycles executed to predict the energy consumed and then chose the best energy efficient schedule.

• The ideal case would be in an online fashion, based on the current load/cycle executed and evaluate the task schedule every second.

Experimental Results (4/6)

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Experimental Results (5/6)

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Experimental Results (6/6)

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Conclusions

• We showed that a linear and Random Forests model

can be used for predicting energy consumption.

• We also proposed a simple scheduler that utilizes

this model to minimize power consumption but still

maintain similar execution time.

• In the future, we propose to come up with a better

mathematical model for scheduler.

• We also propose to use model in an online fashion

and allowing the OS to limit processes that

consume power greater than a fixed limit.

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