Software hardware cooperative power management technique for main memory
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Software-Hardware Cooperative Power Management Technique for Main Memory. Hai Huang, Kang G. Shin University of Michigan Charles Lefurgy, Karthick Rajamani, Tom Keller, Eric Van Hensbergen, Freeman Rawson IBM Austin Research Lab. Motivation.

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Software-Hardware Cooperative Power Management Technique for Main Memory

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Software hardware cooperative power management technique for main memory

Software-Hardware Cooperative Power Management Technique for Main Memory

Hai Huang, Kang G. Shin

University of Michigan

Charles Lefurgy, Karthick Rajamani, Tom Keller, Eric Van Hensbergen, Freeman Rawson

IBM Austin Research Lab


Motivation

Motivation

  • High power dissipation causes a lot problems for many computing systems, especially for large servers

    • High electric and cooling cost

    • Unreliable electronic components

    • Low rack-density

  • Intelligent management of system power is important to ensure these systems can continue to function


Dram a power hog

DRAM: A Power Hog

  • Main memory (DRAM) consumes a significant portion of the total power – which makes it a good candidate to optimize power for

    • E.g., in an IBM mid-range eServer system, around 40% of the total power is consumed by the main memory


Outline

Outline

  • Motivation

  • Background

  • Previous Work

  • A Cooperative Approach

  • Results

  • Conclusion


Outline1

Outline

  • Motivation

  • Background

  • Previous Work

  • A Cooperative Approach

  • Results

  • Conclusion


Background

Background

  • DRAM dissipates power continuously

    • Self-refresh, row/column decoders, amplifiers, data queue, etc.

  • DRAM’s power management capabilities

    • Multiple power states

    • Memory controller is used to implement a simple interface to transition between these states

    • Transitions have non-negligible delays

    • Trade-offs between power and performance


Example ddr

Read/Write

(779.1 mW)

auto

Standby

(275.0 mW)

5ns

5ns

5ns

1000ns

Power-down

(150 mW)

Self-refresh

(20.87 mW)

Example: DDR

Example: Registered 512MB DDR module w/8 devices per rank


Outline2

Outline

  • Motivation

  • Background

  • PreviousWork

    • Software Techniques

    • Hardware Techniques

  • A Cooperative Approach

  • Results

  • Conclusion


Software technique

Process i

context-switched in

Process j

context-switched in

Standby

Rank 0

Rank 1

Rank 2

Rank 3

time

Self-refresh

Self-refresh

Self-refresh

Standby

Self-refresh

Standby

Self-refresh

Software Technique

Process i: uses ranks 0 and 2

Process j: uses rank 3

  • OS can track each process’ virtual-to-physical memory mappings


Hardware technique

Idle time > Threshold

Idle time > Threshold

read/write

Standby

power

Self-refresh

time

Idle time < Threshold

Idle time < Threshold

Hardware Technique

  • Allows for much finer-grained control of power

    • Monitors each memory access

    • Predicts when to transition to lower power modes


Hardware technique problems

Process i

Process j

Process i

Process j

memory

accesses

time

Hardware Technique: Problems

  • Hardware techniques can be easily confused by constant context-switching

    • Different processes would have different memory access behavior, and it takes time for the memory controller to adapt, readapt, readapt…

- Imagine hundreds of parallel processes instead of 2!

- context switching interval ~ 1 msec


Outline3

Outline

  • Motivation

  • Background

  • Previous Work

  • A Cooperative Approach

  • Results

  • Conclusion


Cooperative approach

Cooperative Approach

  • Improve the hardware technique so we don’t have to readapt, readapt, readapt…

    • Need system software cooperation

  • Make the hardware understand the notion of processes

    • At each context switch, OS sends a signal to the memory controller

    • Upon receiving this signal, the memory controller saves and restores its internal registers, which are used for keeping past memory access patterns

    • Essentially, we can now manage power for the current process solely depending on this and only this process’ past memory accesses


Context aware memory controller

Memory controller

CPU

Registers

Registers

Threshold

predictor

Signals

context

switch

Restores

scheduled

process’ CPU

context and

MC context

Saves current

process’

CPU context

MC context

Context-Aware Memory Controller


Cooperative technique per process

Process i

Process j

Process i

Process j

memory

accesses

Cooperative Technique: Per-Process

time


Outline4

Outline

  • Motivation

  • Background

  • Previous Work

  • A Cooperative Approach

  • Results

  • Conclusion


Experimental setup

Experimental Setup

  • Mambo:

    • A full-machine simulator to run various workloads and collect memory traces

  • Memsim:

    • Trace-driven simulator that produces performance and power results for the main memory

  • Workloads:

    • SPECjbb + bzip2 + crafty (low memory-intensive)

    • SPECjbb + art + mcf (high memory-intensive)


Results

Results

Low-memory intensive workload

High-memory intensive workload


Conclusion

Conclusion

  • Cooperative technique

    • Uses 72–75% less power than when no power management is applied, with 11–14% slow-down in average response time

    • Uses 14–17% less power than the hardware technique

    • Uses 16–26% less power than the software technique

    • Has a comparable performance to HW and SW techniques

  • Future Work

    • Communicate hints directly from user processes to the hardware


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