The gem computational system and recent scientific results
1 / 27

The GEM Computational System and Recent Scientific Results - PowerPoint PPT Presentation

  • Uploaded on

The GEM Computational System and Recent Scientific Results. Andrea Donnellan Third International ACES Meeting May 10, 2002. GEM. Data Volumes from Observations. GRACE: 50 MB/day onboard, 8GB/day derived product ECHO: 100 GB/day onboard SRTM: 12 TB raw data,

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about 'The GEM Computational System and Recent Scientific Results' - jarah

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
The gem computational system and recent scientific results

The GEM Computational System and Recent Scientific Results

Andrea Donnellan

Third International ACES Meeting

May 10, 2002


Data volumes from observations
Data Volumes from Observations

  • GRACE: 50 MB/day onboard, 8GB/day derived product

  • ECHO: 100 GB/day onboard

  • SRTM: 12 TB raw data,

  • ICESat: 1 GB/day onboard, 2 GB/day derived

  • SCIGN: 250MB daily - 7.5 GB/day for real time

  • Airborne observations: LIDAR

  • VCL: 2 GB/day onboard, 4 GB/day derived

  • Hyperspectral imagery: 100GB/day raw

  • Imaging LIDAR: >20 GB/day, >40 GB/day

Volumes from models
Volumes from Models

  • Geodynamo model:

    • 1GB of storage for one model run

    • 2010: 5 TB/run

    • Minimal need of 10 runs

  • General earthquake/lithospheric models:

    • 1TB/run

    • 2010: 10 PB/run (multiple scales combined, many regions)

  • Gravity

    • 100 GB/run

    • 2010: 2 TB/run

  • Mantle convection models

    • 1 TB/run

    • 2010: 10PB/run

  • Geomagnetic field models

    • 32 GB/run

    • 2010: 300 GB/run

Where we will be in 2010
Where We Will Be in 2010

  • Multiple solid earth missions flying

  • PetaBytes of data per year gathered in a distributed fashion

  • Data analyzed by widely distributed scientists using widely distributed computational resources

  • Growing need for integration of information from multiple sources on multiple scales into a integrated analysis


  • World-wide computational systems supporting gathering of 3 PetaBytes of data per year, integrating analysis, visualization, simulation, and interpretation.


  • Onboard adaptive processing

  • High space to ground bandwidth of TeraBytes per day per mission

  • Data transmission and handling

  • Reusable capabilities (framework)

  • Data processing (100 Petaflops per mission per year)

Requirements continued
Requirements (continued)

  • Product storage (National Virtual Solid Earth Science Observatory) using cooperative federated databases

  • Distributed computational environment for analysis (interoperable framework, portal)

  • Software tools

  • Hardware

Hardware hierarchical
Hardware (Hierarchical)

  • Large central Petaflop computers with TeraBytes of memory

  • Single sign-on seamless access

  • Distributed computers for decomposable problems

  • Cluster computers (e.g. Beowulf for cost performance)

  • Heterogeneous computational capabilities (e.g. for storage, visualization, computing)


  • Problem Solving Environment

    • Visualization tools

    • Analysis algorithms

    • Data mining

  • Framework

    • Supports software integration into multidisciplinary analysis

    • Interoperability between data,software, and computer systems

Gem servo components
GEM/SERVO Components

  • Visualization

  • Model and algorithm development

  • IT: GRID technologies

  • Computational Environments/PSEs

  • Data handling/archiving

  • Assimilation

  • Datamining/pattern recognition

  • Data fusion

  • High speed networks

  • High end computers

  • Clusters

  • Laptops

  • Cycles needed and other infrastructure

  • Scalable system

Solid Earth Research Virtual Observatory (SERVO)





Tier2 Center

Tier2 Center

Tier2 Center

Tier2 Center

Tier2 Center







1 PB per year data rate in 2010





Tier 0 +1




100 TeraFLOPs sustained

Tier 1

Tier 2

Fully functional problem solving environment

Tier 3

  • Program-to-program communication in milliseconds

  • Approximately 100 model codes





100 - 1000 Mbits/sec

Data cache

Tier 4

Workstations, other portals

  • Plug and play composing of parallel programs from algorithmic modules

  • On-demand downloads of 100 GB in 5 minutes

  • 106 volume elements rendering in real-time

Virtual observatory project
Virtual Observatory Project

  • Solid earth research virtual observatory (SERVO)

  • On-demand downloads of 100 GB files from 40 TB datasets within 5 minutes.

  • Uniform access to 1000 archive sites with volumes from 1 TB to 1 PB

Scaled to 100 sites

Prototype cooperative federated data base service integrating 5 datasets of 10 TB each

Prototype modeling service capable of integrating 5 modules


Decomposition into services with requirements

Prototype 1920x1080 pixels at 120 frames per second visualization service

Prototype data analysis service

Architecture & technology approach

2003 2004 2005 2006 2007 2008 2009 2010


Problem solving environment project
Problem Solving Environment Project

  • Fully functional PSE used to develop models for building blocks for simulations.

  • Program-to-program communication in milliseconds using staging, streaming, and advanced cache replication

  • Integrated with SERVO

  • Plug and play composing of parallel programs from algorithmic modules

Integrated visualization service with volumetric rendering

  • Extend PSE to Include

  • 20 users collaboratory with shared windows

  • Seamless access to high-performance computers linking remote processes over Gb data channels.


Plug and play composing of sequential programs from algorithmic modules

Prototype PSE front end (portal) integrating 10 local and remote services

Isolated platform dependent code fragments

2003 2004 2005 2006 2007 2008 2009 2010


Computational environment
Computational Environment

~100 model codes with parallel scaled efficiency of 50%

~104 PetaFLOPs throughput per subfield per year

~100 TeraFLOPs sustained capability per model

~106 volume elements rendering in real time


Access to mixture of platforms low cost clusters (20-100) to supercomputers with massive memory and thousands of processors

100’s GigaFLOPs


1 Gb/s network bandwidth

2003 2004 2005 2006 2007 2008 2009 2010


Northridge example
Northridge Example

  • Northridge class simulation: 100,000 unknowns, 4000 time steps –> 8 hours on high end workstation.

  • Southern California system: 0.5 km resolution –> 100,000 processor hours or 400 hours (17 days) on a dedicated 256 processor machine.

Coseismic removed from the interferogram
Coseismic Removed from the Interferogram Fill

Postseismic Interferogram

Comparison of insar and seismic anomalies
Comparison of InSAR and Seismic Anomalies Mechanism

  • Similar anomaly shows up in both the postseismic deformation indicated by GPS and InSAR (Donnellan et al) and seismic anomalies identified using Principal Component Analysis (Rundle and Tiampo).

  • Mojave desert shows a similar correlation near Barstow and the Blackwater Fault (Rundle and Tiampo; Peltzer).

Recent gps results
Recent GPS Results Mechanism

  • Similar to pre-seismic velocity field, particularly near the source.

Residuals Mechanism

Anomalous motion at jpl was observed related to the northridge earthquake

Residual Geodetic Longitude (cm) Mechanism

Anomalous Motion at JPL was Observed Related to the Northridge Earthquake

Sierra Madre Fault

1 m

  • JPL is several fault dimensions away from the Northridge rupture.

  • The earthquake probably triggered slip on the Sierra Madre Fault in the upper 0.5 km.

  • Based on additional observations collected near JPL.

  • Later extent of anomaly is unknown due to lack of stations.

California 3d fault simulations
California 3D Fault Simulations Mechanism

  • Faults are shown as light lines, the earthquakes at model year 4526 are shown as dark lines

  • Simulations indicate that major events are clustered in time like the real events.

  • Simulations using a realistic heterogeneous earth structure are computationally intensive.

Modeling faults as interacting systems
Modeling Faults as Interacting Systems Mechanism

Southern California Seismicity

Space-time Stress Diagram

Courtesy John Rundle

  • Transients likely occur as a result of stress redistribution.

  • Are observed on different faults, sometimes a few fault dimensions away.

Conclusions Mechanism

  • 90% of Northridge postseismic motion was aseismic.

  • Afterslip on the mainshock rupture plane responsible for most of the deformation.

  • No evidence for lower crustal relaxation playing a major role in postseismic motions.

  • Recent deformation is consistent with that observed before the earthquake.

More conclusions
More Conclusions Mechanism

  • High velocity gradient largely attributable to a low rigidity basin.

  • Lower crust is a minor player in interseismic and postseismic motion in this region – consistent with a cold lower crust.

  • The earthquake probably triggered slip on the Sierra Madre fault.