Optimizing Power and Energy
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Transcript of Optimizing Power and Energy
Optimizing Power and Energy
Lei Fan, Martyn Romanko
Motivation
31% of TCO attributed to power and cooling Intermittent power constraints
Renewable energy Grid balancing
20% - 30% utilization on average Green: good for the environment Green: saves money
Themes
Hybrid (hardware/software) optimizations Dynamic DRAM refresh rates (Flikker) Dynamic voltage/frequency scaling (MemScale) Distributed UPS management Power cycling (Blink)
Software optimizations Dynamic adaptation (PowerDial)
Flikker: Saving DRAM Refresh-power through Critical Data Partitioning Partitioning of data into critical vs. non-critical Partitioning of DRAM into normal vs. low refresh
rates Programming language construct
Allows marking of critical/non-critical sections Primarily software with suggested hardware
optimizations OS and run-time support Refresh rate optimizations
Flikker
MemScale: Active Low-Power Modes for Main Memory Modern DRAM devices allow for static scaling MemScale adds:
DVFS for MC; DFS for memory channels and DRAM devices Policy based on power consumption and
performance slack
MemScale
Managing Distributed UPS Energy for Effective Power Capping in Data Centers Use of distributed UPSs to sustain peak power loads Based on existing distributed UPS models
Larger batteries needed for longer peak spikes Allows for more servers to be provisioned
Analysis of effect on battery lifetime Argued benefit outweighed cost of extra batteries Lacked detailed analysis on cooling costs
Blink: Managing Server Clusters on Intermittent Power Reducing energy footprint of data centers Power-driven vs. workload driven
Blink: power-driven technique Metered transitions between
High power active states Low power inactive states
Blink
Three policies Synchronous: optimizes for fairness Activation: optimizes for hit rate Load-proportional: both
Unknown effects of power cycling on component lifetime
PowerDial: Dynamic Knobs for Power-Aware Computing When is this applicable for a program?
QoS (accuracy) vs. power/performance tradeoff Subject to system fluctuations
Dynamic tuning of program parameters Adaptable to fluctuations in power/load
Determines control variables Application Heartbeats framework provides
feedback Automatic insertion of API calls
PowerDial
Discussion, Questions?