Power Optimization: Furthering the Mobile Vision

by Karthik Krishnan, Rajshree Chabukswar, and Jun De Vega


Introduction

Understand the power contribution of each major component in an Intel® mobile platform through an in-depth series of whitepapers.

Intel® mobile platforms have been designed to further the mobile vision that includes key areas such as performance, greater battery life, innovative form-factors, and reliable wireless connectivity. Architectural innovation such as Enhanced Intel SpeedStep® Technology is one example of how the CPU maintains high performance while conserving battery life.

New battery technologies are helpful, but still provide limited battery life. Software needs to play a key role in optimizing for power. A mobile platform consists of various components such as a CPU, LCD, HDD, DVD, and chipsets, which individually contribute to the power drain of the notebook. Understanding the power contribution of each major component in the platform provides a better view on the total power usage, and can provide guidance on optimizing power consumption.

In this series of Power Optimization whitepapers, we shall provide developers insight on Intel mobile platform power profiles, and software techniques for optimizing power in specific components (along with the impact on the overall platform). In the first three-part series, we will focus on the following areas:

HDD/SATA I/O:

  • Investigation of the power consumption of a disk during sequential/random reads and the impact of Native Command Queuing, along with analysis on file fragmentation and disk thrashing.

 

DVD I/O:

  • A demonstration of power savings by aggressive buffering to reduce frequent spin-ups during DVD playback.

 

CPU

  • The impact of multithreading on CPU and platform power. Examples of how a high interrupt-rate impact on processor sleep-state residency affects CPU power.

 

Power Measurement Methodology

Measuring power usage of individual components in a mobile platform is not a trivial task. Various tools exist to provide a high-level estimate of the power consumed by a particular mobile platform, but they do not provide the granular details on specific components. A more accurate but invasive way to measure power will be to use data acquisition (DAQ) tools where specific hardware components are instrumented and a more granular power measurement can be logged. The following lists the platform details we used for our analysis, along with the power-measurement methodology.

Hardware

  • Fluke NetDAQ* 2686A
  • Target PC: Intel® Core™ Duo/2GHz Yonah, Jamison Canyon* CRB, 2x512MB DDR2, 40GB SATA 5400 rpm (2.5” mobile), CD/DVD drive, Microsoft Windows* XP Professional SP2
  • Host PC: Any IA32 system

 

Software

  • Test Applications (different applications used)
  • NetDAQ Logger: Fluke DAQ Software v2.2

 

Test Setup:

 

  • Target PC (Napa/Yonah) has a special motherboard (Jamison Canyon CR B) with built-in sensors. For each target component (i.e., the CPU), all sense resistors are wired (soldered) at both ends and connected to a module attached to the NetDAQ unit.
  • The NetDAQ has modules that are attached (individual wires) to the Target PC and measures the current and voltage drop across the sense resistors. NetDAQ is connected to the Host PC via a cross-over network cable.
  • The host PC can be any IA32 system with Microsoft Windows XP and the NetDAQ logger. The logger collects the measured current and voltages and lets the use calculate the average power (W). The sampling interval we used for our entire analysis was 25 milliseconds. The platform power measurement does not include the LCD.

 

Platform Power Profile

The power profile of various components on the mobile platform depends on the usage model. For example, the relative contribution of processor power to the overall platform power will be significant in a CPU-intensive workload, but it will not be a dominant factor while the platform is idling. Furthermore, it may also vary depending on whether both the cores were utilized or not (i.e. single-threaded vs. multithreaded). The following provides an idea of how the profile varies during various usage models. The CPU, memory, and file system tests were run using SiSandra benchmarks*. Note that the platform power below does not include LCD, since we have excluded it from our analysis. (Others include WLAN, HD-Audio, mini-card, ICH, and other peripherals.)

 

As seen above, mobile developers need to have an idea of power drainage depending on the usage model, and target specific components for extending battery life and conserving power. The following sections will include power-related investigation on specific components such as the CPU, HDD, and DVD.

 


About the Authors

Rajshree Chabukswar
Rajshree Chabukswar is a software engineer working on client enabling in the Software Solutions Group that enables client platforms through software optimizations. Prior to working at Intel, she obtained a Masters degree in Computer Engineering from Syracuse University, NY. Her e-mail is rajshree.a.chabukswar@intel.com.

Karthik Krishnan
Karthik Krishnan is a software engineer with the Software Solutions Group at Intel. He holds a Masters degree in Mathematics from the Indian Institute of Technology. His current focus is on power and performance optimization of software applications on dual-core platforms. His e-mail is karthikeyan.krishnan@intel.com.

Jun De Vega
Jun De Vega is an Application Engineer in Intel’s Software and Solutions Group, working on application tuning and optimization for Intel® architecture. He supports enabling of ISV applications on Intel® Mobile and Desktop Platforms. Contact him at rodolfo.de.vega@intel.com.

 


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