balancing power consumption in multiprocessor systems
DESCRIPTION
Andreas Merkel Frank Bellosa System Architecture Group University of Karlsruhe. Balancing Power Consumption in Multiprocessor Systems. EuroSys '06 April 18 - 21, 2006 Leuven, Belgium. State of the Art in Thermal Design. Increasing power dissipation in computer systems - PowerPoint PPT PresentationTRANSCRIPT
University of Karlsruhe, System Architecture Group
Balancing Power Consumptionin Multiprocessor Systems
Andreas Merkel Frank Bellosa
System Architecture GroupUniversity of Karlsruhe
EuroSys '06
April 18 - 21, 2006
Leuven, Belgium
2 University of Karlsruhe, System Architecture Group
State of the Art in Thermal Design
Increasing power dissipation in computer systems Ever increasing cooling costs
3 University of Karlsruhe, System Architecture Group
State of the Art in Thermal Design
Increasing power dissipation in computer systems Ever increasing cooling costs
Alternatives for designing cooling systems: Worst case thermal design
Overprovisioning Unnecessarily high cooling cost
Lower thermal design power Throttling to handle “hot” tasks Performance penalties
4 University of Karlsruhe, System Architecture Group
Alternatives in SMP Systems Multiple processors, running different tasks
Tasks differ in power consumption “hot” and “cool” tasks
Hot and cold processors Unnecessary throttling
T
t
temperaturelimit
5 University of Karlsruhe, System Architecture Group
Alternatives in SMP Systems Multiple processors, running different tasks
Tasks differ in power consumption “hot” and “cool” tasks
Hot and cold processors Unnecessary throttling
T
t
temperaturelimit
throttling
6 University of Karlsruhe, System Architecture Group
Our Approach: Migration instead of Throttling
Leverage cool processors Migrate hot tasks away to a cool processor Combine hot tasks with cool tasks on a processor Balance processor temperatures
T
t
temperaturelimit
migrate
7 University of Karlsruhe, System Architecture Group
Our Approach: Migration instead of Throttling
Leverage cool processors Migrate hot tasks away to a cool processor Combine hot tasks with cool tasks on a processor Balance processor temperatures
T
t
temperaturelimit
8 University of Karlsruhe, System Architecture Group
Our Approach: Migration instead of Throttling
Leverage cool processors Migrate hot tasks away to a cool processor Combine hot tasks with cool tasks on a processor Balance processor temperatures
Contributions: Characterization of tasks Task Energy Profiles Policy for assigning tasks to CPUs Energy-Aware Scheduling
9 University of Karlsruhe, System Architecture Group
Outline
Task Energy Profiles
Thermal Model
Energy-Aware Scheduling
Evaluation
Conclusion
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Characterizing Tasks
Temperature High thermal capacitance of chip and heat sink
Temperature changes slowly Short scheduling intervals CPU temperature:
Mix of multiple tasks' characteristics No knowledge about individual task's characteristics
Power consumption 37W to 61W on Pentium 4 Xeon (2.2 GHz) for
different phases of compute-intensive tasks Characterize tasks by their individual power
consumption
-8
2
12
22
32
42
52
62 aluaddbitcntsmemrwbzip2cjpegdjpeg
idle
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Task Energy Profiles
Definition: Energy consumed during one timeslice Behavior of tasks depends on input data
Online energy estimation required Tasks show periods of constant power consumption
Use knowledge from the past: Energy consumed during last timeslice
Requirement: Determine the amount of energy the CPU consumes
during one timeslice
12 University of Karlsruhe, System Architecture Group
Event-Driven Energy Estimation
Use performance monitoring counters for estimating energy:1) Choose set of suitable events2) Assign amount of energy to each event3) Count events4) Calculate linear combination of the counter values
Error < 10% for real-world integer applications Neglecting floating point applications for lack of suitable
counters
Energyi# event i weight i
13 University of Karlsruhe, System Architecture Group
Thermal Model
Model of processor and heat sink
Relates power consumption to temperature
Models temperature with exponential function
14 University of Karlsruhe, System Architecture Group
Thermal Model
Temperature
Power
Model of processor and heat sink
Relates power consumption to temperature
Models temperature with exponential function
15 University of Karlsruhe, System Architecture Group
Energy Aware Scheduling
Objectives: Minimize the need for throttling processors Avoid unnecessary migrations (cache affinity)
Policy depends on number of tasks per runqueue More than one task
Combine tasks with different characteristics
Energy balancing
One taskMigrate hot task
before CPU overheats
Hot task migration
16 University of Karlsruhe, System Architecture Group
Linear Energy Balancing Goal: Balance CPU temperatures Approach: Balance CPU power consumption
Metric: runqueue power Average of the energy profiles of all tasks in a
runqueue Predicts future energy consumption
Balance power consumption by balancing runqueue power
17 University of Karlsruhe, System Architecture Group
Problem: Runqueue power changes frequently
Whenever tasks start or terminate Whenever tasks block/unblock
Ping-pong effects
Linear Energy Balancing
runqueue power CPU A
runqueue power CPU B
P
t
cool task blocks
migrate hot task
cool task unblocks
migrate hot task
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Runqueue power does not consider tasks that are blocked or have terminated
Heat produced by those tasks is still stored in the chip and the heat sink
Runqueue power cannot distinguish between hot and cool CPUs
Linear Energy Balancing
19 University of Karlsruhe, System Architecture Group
Fit averaging function to thermal model Use exponential average of CPU's power consumption
as metric Thermal power Reflects past energy consumption temperature Calibrate parameters to thermal model
Exponential Energy Balancing
temperature
t
powerthermal power
20 University of Karlsruhe, System Architecture Group
Exponential Energy Balancing Problem:
Task migrations have no immediate effect on thermal power
“Over-balancing”
thermal power CPU A
thermal power CPU B
P
t
migrate hot tasks
21 University of Karlsruhe, System Architecture Group
P
t
Energy Balancing Use both metrics for energy balancing Migrate a hot task from CPU A to CPU B
if A outranges B in both metrics Avoids ping-pong effects Avoids over-balancing
Introduce threshold to defer migrations Hysteresis
migrate
22 University of Karlsruhe, System Architecture Group
Energy Balancing
P
t
Use both metrics for energy balancing Migrate a hot task from CPU A to CPU B
if A outranges B in both metrics Avoids ping-pong effects Avoids over-balancing
Introduce threshold to defer migrations Hysteresis
23 University of Karlsruhe, System Architecture Group
Hot Task Migration
Only one task in a runqueue Balancing not possible
Migrate task to cooler CPU if CPU temperature comes close to maximum
Search for cool target CPU Idle CPU
Migrate task CPU executing cool task
Swap tasks
24 University of Karlsruhe, System Architecture Group
Evaluation
Implementation of energy aware scheduling for the Linux kernel
Test system: 8-way Pentium 4 Xeon, 2.2 GHz
Workload scenarios Power consumption of tasks ranging from 37W to
61W Measure throughput by counting number of tasks
finished per time unit Expect increase in throughput if energy-aware
scheduling is enabled
25 University of Karlsruhe, System Architecture Group
Evaluation: Energy Balancing
Disabled Enabled
Scenario: 8 CPUs executing different tasks Energy balancing increases throughput by 4.7%
60W
40W
20W
60W
40W
20W200s 400s 600s0s 200s 400s 600s0s800s 800s
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Evaluation: Hot Task Migration
Disabled Enabled
One hot task Hot task migration avoids throttling
60W
40W
20W
60W
40W
20W
50s 100s 150s0s 200s 50s 100s 150s0s 200s
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Homogeneous vs. Heterogeneous Workloads
Workloads consisting of different combinations of memrw (38W), pushpop (47W), and bitcnts (61W)
9/0/9 8/2/8 7/4/7 6/6/6 5/8/5 4/10/4 3/12/3 2/14/2 1/16/1 0/18/00%
5%
10%
15%
scenario (#memrw/#pushpop/#bitcnts)
incr
ea
se in
th
rou
gh
pu
t
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Conclusion Need to throttle processors exceeding thermal design
power Approach: leverage cool processors instead of throttling
hot processors Characterize tasks by power consumption
Energy profiles
Use energy profiles for energy-aware scheduling Energy balancing Hot task migration
Reduce thermal imbalances
Minimize throttling increase duty cycles