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Presentation for the……
Dr. Gregory HidleyCalifornia Institute for Telecommunications and Information
Technology, UCSD
Project GreenLight
ICT is a Key Sector in the Fight Against Climate Change
Applications of ICT could enable emissions reductions
of 7.8 Gt CO2e in 2020, or 15% of business as usual emissions.
But it must keep its own growing footprint in check and overcome a number of hurdles
if it expects to deliver on this potential.
www.smart2020.org
ICT Industry is Already Actingto Reduce Carbon Footprint
Application of ICT Can Lead to a 5-Fold GreaterDecrease in GHGs Than its Own Carbon Footprint
Major Opportunities for the United States*– Smart Electrical Grids– Smart Transportation Systems– Smart Buildings– Virtual Meetings
* Smart 2020 United States Report Addendum www.smart2020.org
“While the sector plans to significantly step up the energy efficiency of its products and services,
ICT’s largest influence will be by enabling energy efficiencies in other sectors, an opportunity
that could deliver carbon savings five times larger than the total emissions from the entire ICT sector in 2020.”
--Smart 2020 Report
GreenLight Motivation: The CyberInfrastructure (CI) Problem
• Compute energy/rack : 2 kW (2000) to 30kW in 2011
• Cooling and power issues now a major factor in CI design
• IT industry is “greening” huge data centers … but today every $1 spent on local IT
equipment will cost $2 more in power and overhead
• Academic CI is often space constained at departmental scale
• Energy use of growing departmental facilities is creating campus crises of space,
power, and cooling
• Unfortunately, little is known about how to make shared virtual clusters energy
efficient, since there has been no campus financial motivation to do so
• Challenge: how to make data available on energy efficient deployments of rack scale
hardware and components?
The NSF-Funded GreenLight ProjectGiving Users Greener Compute and Storage Options
• PI is Dr. Thomas A. DeFanti• $2.6M over 3 Years to construct GreenLight Instrument
– Start with Sun Modular Data Center• Sun Has Shown up to 40% Reduction in Energy• Measures Temperature at 5 Levels in 8 Racks• Measures power Utilization in Each of the 8 Racks• Chilled Water Cooling input and output temperatures
– Add additional power monitoring at every receptacle– Add web and VR interfaces to access measurement data
• Populate with a variety of computing clusters and architectures– Traditional compute and storage servers– GP GPU arrays and specialized FPGA based coprocessor systems– DC powered servers– SSD equipped systems
• Turn over to investigators in various disciplines • Measure, Monitor and Collect Energy Usage data
– With the goal of maximizing work/watt
The GreenLight Project: Instrumenting the Energy Cost of Computational Science
• Focus on 5 Communities with At-Scale Computing Needs:– Metagenomics– Ocean Observing– Microscopy – Bioinformatics– Digital Media
• Measure, Monitor, & Web Publish Real-Time Environmental Sensor Output– Via Service-oriented Architectures– Allow Researchers Anywhere To Study Computing Energy Cost– Enable Scientists To Explore Tactics For Maximizing Work/Watt
• Develop Middleware that Automates Optimal Choice of Compute/RAM Power Strategies for Desired Greenness
• Partnering With Minority-Serving Institutions Cyberinfrastructure Empowerment Coalition
Source: Tom DeFanti, Calit2; GreenLight PI
GreenLight Goals: More Work/Watt
• Build a full-scale virtualized device, the GreenLight Instrument
• Measure then minimize energy consumption
• Develop middleware to automate optimal choice of compute/RAM power
strategies
• Discover better power efficiency configurations and architectures
• Teach future engineers who must scale from an education in Computer Science
to a deeper understanding in engineering physics
• Measure, monitor, and make publicly available, via service-oriented
architectures, real-time sensor outputs
• Focus on 5 communities: metagenomics, ocean observing, microscopy,
bioinformatics, and digital media
• Allow researchers anywhere to study the energy cost of at-scale scientific
computing
GreenLight Research activitiesLeading to Greener CI Deployments
• Computer Architecture – FPGA, GP GPU systems – Rajesh Gupta/CSE
• Software Architecture – Virtualization, memory management, networking and modeling– Amin Vahdat, Ingolf Kruger/CSE
• CineGrid Exchange – mixed media storage, streaming, and management– Tom DeFanti/Calit2
• Visualization – Using 2D and 3D modeling on display walls and CAVEs – Falko Kuster/Structural Engineering, Jurgen Schulze/Calit2
• Power and Thermal Management – Tajana Rosing/CSE
• DC Power Distribution– Greg Hidley/Calit2
http://greenlight.calit2.net
Calit2/UCSD [http://greenlight.calit2.net]
Monitoring, Modeling and Management
GLIMPSEDecision Support System
http://glimpse.calit2.net
Situational Awareness
Calit2/UCSD [http://greenlight.calit2.net] 12
“Tap” for details
Dashboard interface
Power utilization
Multiple perspective
s
Enterprise reach
Datacenter vitals
2010.08.20 Calit2/UCSD [http://greenlight.calit2.net] 13
Input/Output
sampling
Live/Average
data
Live Temperatur
e
Live/average
Fan speeds
Environmentals
Heat Exchangers
Domain specific views
2010.08.20 Calit2/UCSD [http://greenlight.calit2.net] 14
Control elements
Real-time heatmap
Realistic models
Airflow dynamics
2010.08.20 Calit2/UCSD [http://greenlight.calit2.net] 15
Live fan speeds
Airflow dynamics
Heat distribution
2010.08.20 Calit2/UCSD [http://greenlight.calit2.net] 16
Combined heat + fans
Realistic correlation
Heat Trends
Calit2/UCSD [http://greenlight.calit2.net] 17
Trends over past 24h
Heat exchangers
Hotspot identification
Past changes in airflow
Calit2/UCSD [http://greenlight.calit2.net] 18
Fan slicesrpm
Potential for failures
Trends over past 24h
Heat distribution
changes
Power spikes
Calit2/UCSD [http://greenlight.calit2.net] 19
1 minuteresolution
Unused asset
Average load
IT assets
Peak computation
Computation zone
Zoom-in Analysis
Calit2/UCSD [http://greenlight.calit2.net] 20
History over several days.
Zoom on desired time range.
Hint on each sample point.
Automatic average area.
Multiple sensors per asset with up to 1 min sampling resolution.
The GreenLight Project Focuses on Minimizing Energy for Key User Communities
• Microbial Metagenomics• Ocean Observing• Microscopy• Bioinformatics• Digital Media—CineGrid Project
– Calit2 will Host TBs of Media Assets in GreenLight CineGrid Exchange to Measure and Propose Reductions in the “Carbon Footprint” Generated by:• File Transfers and • Computational Tasks
– Required for Digital Cinema and Other High Quality Digital Media Applications
GreenLight Project: Putting Machines To Sleep Transparently
22
Peripheral
Laptop
Low power domain
Network interface
Secondary processor Network interface
Managementsoftware
Main processor,RAM, etc
IBM X60 Power Consumption
0
2
4
6
8
10
12
14
16
18
20
Sleep (S3) Somniloquy Baseline (LowPower)
Normal
Po
we
r C
on
su
mp
tio
n (
Wa
tts
)
0.74W(88 Hrs)
1.04W(63 Hrs)
16W(4.1 Hrs)
11.05W(5.9 Hrs)
Somniloquy Enables Servers
to Enter and Exit Sleep While Maintaining
Their Network and Application Level Presence
Rajesh Gupta, UCSD CSE; Calit2
Improve Mass Spectrometry’s Green Efficiency By Matching Algorithms to Specialized Processors
• Inspect Implements the Very Computationally Intense MS-Alignment Algorithm for Discovery of Unanticipated Rare or Uncharacterized Post-Translational Modifications
• Solution: Hardware Acceleration with a FPGA-Based Co-Processor– Identification and Characterization of Key Kernel for
MS-Alignment Algorithm– Hardware Implementation of Kernel on Novel FPGA-based Co-
Processor (Convey Architecture)• Results:
– 300x Speedup & Increased Computational Efficiency
Large Savings in Energy Per Application Task
Virtualization at Cluster Level for Consolidation and Energy Efficiency
• Fault Isolation and Software Heterogeneity, Need to Provision for Peak Leads to:– Severe Under-Utilization– Inflexible Configuration– High Energy Utilization
• Usher / DieCast enable:– Consolidation onto Smaller
Footprint of Physical Machines– Factor of 10+ Reduction in
Machine Resources and Energy Consumption
Original Service
Virtualized Service
Source: Amin Vadhat, CSE, UCSD
Usher
DC Power: UCSD is Installing Zero Carbon EmissionSolar and Fuel Cell DC Electricity Generators
San Diego’s Point Loma Wastewater Treatment Plant Produces Waste Methane
UCSD 2.8 Megawatt Fuel Cell Power Plant Uses Methane
2 Megawatts of Solar Power Cells at UCSD, 1 MW to be
Installed off campus
Available Late 2011
Zero Carbon GreenLight Experiment:DC-Powered Modular Data Center
• Concept—Avoid DC to AC to DC Conversion Losses– Computers Use DC Power Internally– Solar and Fuel Cells Produce DC– Both Plug into the AC Power Grid– Can We Use DC Directly (With or Without the AC Grid)?
• DC Generation Can Be Intermittent – Depends on Source
• Solar, Wind, Fuel Cell, Hydro– Can Use Sensors to Shut Down or Sleep Computers– Can Use Virtualization to Halt/Shift Jobs
• Experiment Now Underway– Collaboration with Sun, EPRI, DPTI and LBNL– NSF GreenLight Year 2 and Year 3 Funds
Source: Tom DeFanti, Calit2; GreenLight PI
Sun Box <200kWatt
DC Power Distribution• Today's data center AC based distribution model:
– 480-V AC power from the grid is converted to DC to charge a battery-based UPS
– DC stream from the UPS is converted to AC and transformed to 208-VAC for distribution
– The above AC is then rectified back to 380-VDC in each server's power supply
• DC distribution offers a comparatively simpler scheme:– a single rectifier turns 480-V AC into 380-V DC that both charges the UPS and
supplies the servers.
Energy Power Research Institute (EPRI) and Duke Energy Corp. measured a 15 percent reduction in power consumption in a test of 380-V DC distribution at the utility's Charlotte, N.C., data center. Net energy savings could be twice that, they claim, once the cooler-running equipment's reduced air conditioning burden is factored in.
UCSD Two Rack DC Experiment
Questions?